{"as_of":"2026-08-14T12:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:73fddb9922315e30015c289470c32348b25192c30b14849b6dba0dc246cd4f42","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T01:07:34.844695Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.11869/citation-record","integrity":"/paper/2506.11869/integrity","json":"/paper/2506.11869/citation-record.json","paper":"/paper/2506.11869"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.815655Z","title":"An introduction to probabilistic graphical models, 2003","venue":null,"work_id":"d9c67570-17c2-412b-ac9c-5cf508e01c93","year":2003},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.587731Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:3abfb1f1c30f2483f436fccecf518c93953c0e5e64713efe720d9810f7eac42f","observation_id":"d5e36c6e-0bb7-447e-be99-248f30fc3395","resolution":{"observed_at":"2026-08-07T01:07:35.819259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:34.592268Z","title":"Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.592268Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:f045558faaca3f2aec9a57d33a4c89e6bef346e1b5196e961d0faeda1bc8d3ba","observation_id":"09d0c10e-b599-4e97-9de3-dde9c995ac1f","resolution":{"observed_at":"2026-08-07T01:07:34.592268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.796697Z","title":"Wang and George Y","venue":null,"work_id":"b5f1dc0e-0127-4608-b197-6c491eb230df","year":1987},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.596017Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:cf3d58820de72fa06b45d5591b4ac53b9d9fa15dee0685cf8c6dda2712fd855f","observation_id":"1e7c48a5-b09c-4af7-8c48-76e97947d945","resolution":{"observed_at":"2026-08-07T01:07:35.800145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.785710Z","title":"Estimation and prediction for stochastic blockmodels for graphs with latent block structure","venue":null,"work_id":"76197216-f1de-4b2d-a510-c93dbbb9d032","year":1997},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.599699Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:09c6c287816b9b75e2b1ec915c6b07d41b9ba48edf8c9a8d1f993b5bb48058b5","observation_id":"d60c70f0-8166-421e-8ebe-d54215d62462","resolution":{"observed_at":"2026-08-07T01:07:35.789377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.774568Z","title":"Community detection and stochastic block models: recent developments","venue":null,"work_id":"f2d65fc8-6803-4387-993e-168c649cd756","year":2017},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.603228Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:e460b407fb7c9d4e1668b1a6090ea8de8dadb848de77b7c0b2b49f0de51ed27a","observation_id":"6fdf4fe4-6216-4713-917e-219229237503","resolution":{"observed_at":"2026-08-07T01:07:35.778466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.763770Z","title":"The graph neural network model","venue":null,"work_id":"95ee7d54-12a0-4fd2-bd68-fd1e51790b66","year":2009},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.606833Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:ab5cf336735f4c6924cc4ce813cbcbc9efdbb8cbdf01f023dbdfad8c78ab87ee","observation_id":"49f02f15-3c2d-4f23-aa4c-b90db0a7da81","resolution":{"observed_at":"2026-08-07T01:07:35.767198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.752834Z","title":"Convolutional neural networks on graphs with fast localized spectral filtering","venue":null,"work_id":"45273ebb-59c8-4451-8040-891b9b4992ff","year":2016},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.610735Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:b5be74b8f73e6cdd48334d42dea85fd91d19edafe577f704ed6d6d1f43d91f29","observation_id":"5630c07b-7946-4e32-b425-c7960138c729","resolution":{"observed_at":"2026-08-07T01:07:35.756444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.741134Z","title":"Bronstein","venue":null,"work_id":"f0335235-e789-4803-b2c9-4f8cb414c10a","year":2017},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.613979Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:6356c79256ddb79dd0f440727257f66527112b7c2a2fb740779a3da2a8df7611","observation_id":"4eeb8a4a-ebf9-41d6-a2c6-c5c29ab31ebb","resolution":{"observed_at":"2026-08-07T01:07:35.745031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.730792Z","title":"Message passing all the way up, 2022","venue":null,"work_id":"efbf11cc-c4db-48c7-a622-2bf5522b395e","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.617240Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:51a0b60f756114f80b1905d3637adf6d0e8267de8dc6b69d56ff4aeec865e4ee","observation_id":"eb5f550e-d697-485f-bab7-17c5a8dfc762","resolution":{"observed_at":"2026-08-07T01:07:35.733973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.720017Z","title":"Revisiting heterophily for graph neural networks","venue":null,"work_id":"036c9abf-44d8-4857-8c19-c5394285b6a7","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.620786Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:82c5b8eadf0e3a0e044d47c91ed054eab262cfaaff45ee3b3d70796641c3ca6d","observation_id":"3b4df54d-64b7-468b-b4db-1936fa61c5d2","resolution":{"observed_at":"2026-08-07T01:07:35.723573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.708964Z","title":"Bronstein","venue":null,"work_id":"b7699140-19b2-462b-aaa1-2c6046fcafb2","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.624006Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d00fdfeed69edfc8abe3b62c463fda0724b8bcc593783b9f72da61673ac020fe","observation_id":"a5934228-1a91-4a2d-a0a2-d8d1f4793ee6","resolution":{"observed_at":"2026-08-07T01:07:35.712297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.697644Z","title":"Understanding heterophily for graph neural networks","venue":null,"work_id":"96aed16c-ae41-4126-a2d9-299298da1022","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.627774Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:da38c75aa48d3afe45e0d2d6a8a742969d6309ed88eb011011ea915dcdf096b5","observation_id":"fa424c58-df1a-4354-a1d9-bc8ad273ba57","resolution":{"observed_at":"2026-08-07T01:07:35.701465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.686660Z","title":"Beyond homophily in graph neural networks: current limitations and effective designs","venue":null,"work_id":"ac4d1b02-74d3-4a79-8c10-0a57dadd0325","year":2020},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.631111Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:c06e57f6c19b5fe1d04739747eb982c99510d241d57c3cf4b144c81833a28961","observation_id":"ad6ebfef-c4fb-49bf-986d-1706d6569f90","resolution":{"observed_at":"2026-08-07T01:07:35.690275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.675368Z","title":"Finding global homophily in graph neural networks when meeting heterophily","venue":null,"work_id":"36f4d13b-55fd-44cd-89af-b8e30d912088","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.634412Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:980a29c18e62938752f76e5e48dd307baa7cffd744c793709b47b955c0313e5c","observation_id":"06a78d99-2ad9-4f7a-bcd1-4c2a739a47ed","resolution":{"observed_at":"2026-08-07T01:07:35.679206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.09618","last_updated":"2024-07-12T18:04:32Z","snapshot_observed_at":"2026-08-14T03:13:17.930494Z","submitted_at":"2024-07-12T18:04:32Z","title":"The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.09618","snapshot_observed_at":"2026-08-07T01:07:34.637666Z","title":"The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.637666Z"},"links":{"cited_paper":"/paper/2407.09618","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:ee575c5242d16fab6d4f7709a6dc8c5d7be674a3fd2d26b63007ba9d50137495","observation_id":"59cbb80b-cd2b-449f-8670-116798668a8f","resolution":{"observed_at":"2026-08-07T01:07:34.637666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17475","last_updated":"2024-09-26T02:19:48Z","snapshot_observed_at":"2026-08-13T18:43:57.729345Z","submitted_at":"2024-09-26T02:19:48Z","title":"On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2409.17475","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.17475","snapshot_observed_at":"2026-08-07T01:07:35.073768Z","title":"On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks","venue":"cs.LG","work_id":"af5e8c78-9be8-4b8d-bfed-c708192b1082","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.641273Z"},"links":{"cited_paper":"/paper/2409.17475","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:14cfacdfb3cd5b35c46eb02672ef6a4e49778ed4ad4d9e360a2e2b48c268c4b5","observation_id":"b30da4a9-417b-4dff-a83c-d177a3e173c8","resolution":{"observed_at":"2026-08-07T01:07:35.079364Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:34.644880Z","title":"Insights from network science can advance deep graph learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.644880Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:e0ff63ac8ac5271c2b693f0162022cf5d0439f4c8a152622603d84a1f815f229","observation_id":"1febad34-8de4-4ea3-8daf-eb70ae63606f","resolution":{"observed_at":"2026-08-07T01:07:34.644880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.05738","last_updated":"2019-05-14T17:32:12Z","snapshot_observed_at":"2026-07-06T07:52:57.535245Z","submitted_at":"2019-05-14T17:32:12Z","title":"Stochastic Blockmodels meet Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"1905.05738","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.05738","snapshot_observed_at":"2026-08-07T01:07:34.973013Z","title":"Stochastic Blockmodels meet Graph Neural Networks","venue":"cs.LG","work_id":"bd855269-0539-4b40-83e1-63e495f22847","year":2019},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.648237Z"},"links":{"cited_paper":"/paper/1905.05738","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:b48b878a2d7664c70d7f1e09421a173f03843f177d84f8cee4b4c516033fc463","observation_id":"4bcacaef-5b73-4207-b631-f9e48b53f058","resolution":{"observed_at":"2026-08-07T01:07:34.978988Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.663669Z","title":"The deep latent position block model for the block clustering and latent representation of networks, 2024","venue":null,"work_id":"6d7ea692-cac5-452a-9638-1168d9985e1a","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.651895Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:19382ac047da17751e6833503f1f06761cabf08032cc0c925a11ab082f853223","observation_id":"03d5649c-8018-47d4-9bd0-2a0f50a6496a","resolution":{"observed_at":"2026-08-07T01:07:35.667525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.651671Z","title":"Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks","venue":null,"work_id":"3013f593-403f-48c1-90e3-7d2e08d570b3","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.655823Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:6c95c6f15bc5ea6e2305b835828bd99e96c9a460847581337f90e70c04a4a053","observation_id":"b994f33b-b2c3-4c0f-932a-9d10fe78b092","resolution":{"observed_at":"2026-08-07T01:07:35.655231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.640234Z","title":"Inference in probabilistic graphical models by graph neural networks","venue":null,"work_id":"f04aa020-371a-42e1-a57c-f7a72bab9d08","year":2019},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.659440Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:b452721bbf8d6621b7e92a213d0e169e1bbe69d5b1c4f8cc0528e8e70efa7849","observation_id":"e19143c2-e002-485a-bc10-6fa3d983c66b","resolution":{"observed_at":"2026-08-07T01:07:35.643999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.628745Z","title":"GNNs getting comfy: Community and feature similarity guided rewiring","venue":null,"work_id":"ffd6bdf3-cb1e-4901-b7ed-ca37e71b6088","year":2025},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.662666Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:605988ff2801f4c4c902c9ddc6f246a07ad95a9dc887672ebb59c6e152b8fc40","observation_id":"d9e51af5-1a65-4eee-88a8-197c600ab668","resolution":{"observed_at":"2026-08-07T01:07:35.632561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.617267Z","title":"Revisiting graph neural networks: All we have is low-pass filters, 2019","venue":null,"work_id":"8229daf3-8e09-4ed1-b814-5eaad80aef37","year":2019},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.666168Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d5edfabb8440580f63e5b47ca2025b65c781ccf8d11b76c1a0569e50086a57df","observation_id":"509d4133-50c2-44b9-9dfd-8efc5f8b161d","resolution":{"observed_at":"2026-08-07T01:07:35.620879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.605536Z","title":"On the bottleneck of graph neural networks and its practical implications","venue":null,"work_id":"d9d9ac96-33f6-4922-9db5-fd90e1737b17","year":2021},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.669395Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:6c9783e0c7ae178269140fb8fb747d603c0c5729b3b5aecff7f0d7e6da32e018","observation_id":"8ed0f25b-fd5f-463f-8e3e-b9d6a7ff2d73","resolution":{"observed_at":"2026-08-07T01:07:35.609247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:34.672601Z","title":"Graph clustering with graph neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.672601Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:087fe67b8d158ed806cf003bf8b210933b1e1394c293572649649a36f80710ca","observation_id":"216f54f5-f4b8-4d3a-b5d1-1ff29d2bc755","resolution":{"observed_at":"2026-08-07T01:07:34.672601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.586616Z","title":"Duranthon and Lenka Zdeborov’a","venue":null,"work_id":"fe2da028-a95f-4ca0-b398-54d72bb396ca","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.675939Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:81ed32d7e27da5884f1654dc1bf8275008d500d1501e6f892b985abe906b7cf1","observation_id":"256316e5-ae40-47db-883c-f7a2621a819d","resolution":{"observed_at":"2026-08-07T01:07:35.589840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.575777Z","title":"The ground truth about metadata and community detection in networks","venue":null,"work_id":"1c962f53-c25f-41e0-9db3-b0e87cdb820d","year":2017},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.679100Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:39cfb3f3b3b352d1ef01bfbbafa1c472a5de26d3072280e20a410b184ce9c1dd","observation_id":"6f38ba7c-32ff-40fa-bbff-a7aeb50b635c","resolution":{"observed_at":"2026-08-07T01:07:35.579244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.564591Z","title":"Structure and inference in annotated networks","venue":null,"work_id":"a67ab6e5-7116-4b36-9c84-4abbed2fa1ed","year":2016},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.682386Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:438f5cd787f635dd7c6c815cc5404cffbfba1952bd112eb506fe27eb7712a7c8","observation_id":"5022a10f-052b-48af-a758-df480cd5d04c","resolution":{"observed_at":"2026-08-07T01:07:35.568379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.553447Z","title":"Community detection with node attributes in multilayer networks","venue":null,"work_id":"71bbc05c-f82f-4b19-9e96-94f042fff8ca","year":2020},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.685944Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:1536b3ef6e6b43f960e7dda01e1bc5c3794dc00c353df7fe006708feccef49b6","observation_id":"5342d4b5-744e-4e11-b2fa-9a1e2bc15c3b","resolution":{"observed_at":"2026-08-07T01:07:35.557354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.542481Z","title":"Structure and inference in hypergraphs with node attributes","venue":null,"work_id":"a58a6ff4-c1f8-4c38-a84b-39717dc3f83a","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.689482Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:40c59ae9e6836e828e5269a89f89879644eab6098c49acb179862b6702b5fa36","observation_id":"f7f0a2d1-66a9-4a06-97d1-3a06aed7c0b7","resolution":{"observed_at":"2026-08-07T01:07:35.546186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.531496Z","title":"Power, Daniel B","venue":null,"work_id":"c027038b-31d9-4a10-a48b-9705a9193e5a","year":2017},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.692783Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:6160e561cac44a9ec63b5be767714bb60f2a0786d204f3569dd7ea927d72b04a","observation_id":"0c51688b-5200-411a-982d-6c9b64514dc5","resolution":{"observed_at":"2026-08-07T01:07:35.534959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.520291Z","title":"Efficient monte carlo and greedy heuristic for the inference of stochastic block models","venue":null,"work_id":"d38f8ea4-3158-4999-a460-5b76cf54d57e","year":2014},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.696268Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d297d13c9a3673b27e0e6e551d2fd2ef6a818bcdfafb135d4d3f537a476a6ef7","observation_id":"b02e774a-73c9-4199-83f9-974063b96753","resolution":{"observed_at":"2026-08-07T01:07:35.523874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.508467Z","title":"Pairre: Knowledge graph embeddings via paired relation vectors","venue":null,"work_id":"bef38681-ff60-4c34-9e4b-3556f54fa306","year":2020},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.699935Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:75fcdb06a35458b7c2f1ec4d72b69ba4e4f02d4c5f9e689736b6d0f168852d50","observation_id":"25699b87-6110-4051-9841-19370a5e7149","resolution":{"observed_at":"2026-08-07T01:07:35.512851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.496900Z","title":"Automated concatenation of embeddings for structured prediction","venue":null,"work_id":"641f437f-65a3-4e32-98cd-d09efd577d93","year":2020},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.703233Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:79d5a6372fa0ffda2805cb6d23cc0864142b6e4d3ffb2fcb1db65529c1e81cef","observation_id":"21b4f2ae-7573-43eb-b07f-33e2ae266351","resolution":{"observed_at":"2026-08-07T01:07:35.500741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.485447Z","title":"MacQueen","venue":null,"work_id":"e65e0a00-41bb-40ed-aa92-fd075d1c0072","year":1967},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.706735Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:0b44d952c2b3505c03d438b93f824c6862aa77863e769d44936cf5e98811218d","observation_id":"6503d60f-ad11-432c-8e1d-a4aaffb39524","resolution":{"observed_at":"2026-08-07T01:07:35.489007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.474191Z","title":null,"venue":null,"work_id":"e583a5c9-ccdc-4c32-859d-657bad74f8c8","year":1982},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.710245Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:c1548189949dd740cb405b5e69f6b0ca935412c0e24370eefd91b2b56567a678","observation_id":"25bec240-8a74-4158-8685-41de49b6181a","resolution":{"observed_at":"2026-08-07T01:07:35.477550Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.463101Z","title":"Graph Attention Networks","venue":null,"work_id":"d37c6a48-af42-4a63-a40f-7445b89ca264","year":2018},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.715046Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:3d6c6bc1ea7db2176b441ad9e97adbb8f79bfc2b8934605afd6af2e31f7e64c2","observation_id":"37115902-1c12-4b80-92d8-f8c1bb978ccf","resolution":{"observed_at":"2026-08-07T01:07:35.466680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.452489Z","title":"Kipf and Max Welling","venue":null,"work_id":"74f81f2a-bb50-4f18-bc62-486de8294ffd","year":2016},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.718710Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:525eb2090868b9365ad073179f49d59601b3a77e956671bf6f0a2d54b32c0459","observation_id":"1ab244cb-ea1f-480f-be77-e0926077a4eb","resolution":{"observed_at":"2026-08-07T01:07:35.455675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.441406Z","title":"Beyond homophily in graph neural networks: Current limitations and effective designs","venue":null,"work_id":"3097b576-b9d4-4bd1-bf30-6363b60ce4ee","year":2020},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.721942Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:5a61ffa41ee965709aa8bd97701ffb9a8fc101a97db0778ea167318c574b52a8","observation_id":"1692204f-e229-43bc-b0c5-41295d45d5f4","resolution":{"observed_at":"2026-08-07T01:07:35.444907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.430680Z","title":"Konstantin Rusch, Michael M","venue":null,"work_id":"fd632a63-ed54-4e49-b87d-d09ca4be75b6","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.725267Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:9261f88304a9cf3e5c59b547b032710adf4cc41bf112953e798a52e4b9cdf194","observation_id":"5adb7184-eeb7-4edc-9960-987bc9c72447","resolution":{"observed_at":"2026-08-07T01:07:35.434072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.419379Z","title":"Demystifying oversmoothing in attention-based graph neural networks","venue":null,"work_id":"35b5f6a4-97f1-407f-a5a3-1ada4aa0c8bf","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.729517Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:496f2429e0a86e1e7b997d71f72e41afddb90abf956de0b1c2d1ec62b11f36b0","observation_id":"4e0c026e-099c-43af-b97f-ffef5cf2a0fa","resolution":{"observed_at":"2026-08-07T01:07:35.423388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.407162Z","title":"Graph neural networks exponentially lose expressive power for node classification","venue":null,"work_id":"a42ee4b2-6653-4c1b-8f41-6980e1eec277","year":2019},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.732826Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d169c41e98094197c208cd4258a11568ea9b273776262189b8fcdd1ff2194b9c","observation_id":"cc3faa03-0627-4bc7-a576-5591ec2636a3","resolution":{"observed_at":"2026-08-07T01:07:35.410661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-08-14T09:35:00.453149Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-07T01:07:34.736049Z","title":"A note on over-smoothing for graph neural networks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.736049Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:b5b1ce249514509a2b1ed0ca6edad6c14ee754f63bf9baf0b43d9fd71af4d9d8","observation_id":"622366f4-bb0c-4cc6-baee-655ff236e03d","resolution":{"observed_at":"2026-08-07T01:07:34.736049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.396365Z","title":"Cohen, and Ruslan Salakhutdinov","venue":null,"work_id":"5575f237-0b8c-4300-a1f3-1f82e04e7a29","year":2016},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.739937Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:9e8484566666b95dc2d95ae002f0970c557c3dfb88da3ea3c8d0136ca77a7b6f","observation_id":"92a32535-a125-4643-ac34-6e6104a41a60","resolution":{"observed_at":"2026-08-07T01:07:35.399786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.385175Z","title":"Predicting multicellular function through multi-layer tissue networks","venue":null,"work_id":"8e4a07b8-4b3c-458f-896f-d08e406807f6","year":1916},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.743157Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:c94d465f9f7bec55244fe482a596f43de36cda8332c29eaead9f1b012cf4a364","observation_id":"1412bb47-eaf8-4d1a-95ac-35ee42daa1fd","resolution":{"observed_at":"2026-08-07T01:07:35.388756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.374097Z","title":"Long range graph benchmark","venue":null,"work_id":"44be41a2-f68b-4816-bcd3-8f0b11e1670b","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.746432Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:a0b53d240efa9407cbdcf93ce399fe9d10d641b7e0f285864bdef316e075d82b","observation_id":"9c24c0bf-e189-4046-85e8-8ecd0182c75e","resolution":{"observed_at":"2026-08-07T01:07:35.377584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.363339Z","title":"Vishwanathan","venue":null,"work_id":"0acdcedd-bb9c-4b5f-a02a-0365de74a6ae","year":2015},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.749838Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:793f310ae3d9a8168652affdab6a05ad432aec5bc027959146a97ee20f436f5d","observation_id":"9ad5a343-440a-4f35-97cc-8a88c128f623","resolution":{"observed_at":"2026-08-07T01:07:35.366721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.352143Z","title":"Adamic and Natalie Glance","venue":null,"work_id":"6bb4f050-befa-4e07-a715-864458075967","year":2004},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.752909Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d181455795b97e940710c20d03883c3db80ad1220201824eb599e292c73409b9","observation_id":"96f0dfa4-0f90-4bb6-8918-bce057446082","resolution":{"observed_at":"2026-08-07T01:07:35.355434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.341097Z","title":"Benson, Jure Leskovec, and David F","venue":null,"work_id":"1045a3d8-19b8-4293-ad69-d10bed489d73","year":2017},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.756220Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d7e2ea3402cbba152a194dec20aa241115144682553d727518468465b88a3332","observation_id":"b9cfaf04-4dcc-484b-898d-67661a5c3a9e","resolution":{"observed_at":"2026-08-07T01:07:35.344626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05287","last_updated":"2020-02-14T01:47:35Z","snapshot_observed_at":"2026-08-10T23:39:48.575656Z","submitted_at":"2020-02-13T00:03:09Z","title":"Geom-GCN: Geometric Graph Convolutional Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.05287","snapshot_observed_at":"2026-08-07T01:07:34.759359Z","title":"Geom-gcn: Geometric graph convolutional networks","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.759359Z"},"links":{"cited_paper":"/paper/2002.05287","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:1f0404960e7cbc6a7d3e01de8527e6ea64b9eb0379089e2a733d6682d12963c5","observation_id":"a07b7555-13e3-45cc-af4d-05f3fc07a25d","resolution":{"observed_at":"2026-08-07T01:07:34.759359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T01:07:34.762867Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.762867Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d84e7f48823954f0aba40cb5e6fe9d38bbe64c7b6f5e74f0dfa1775714e1db86","observation_id":"65760c64-ee05-4adc-bdf5-d46adfb9d01c","resolution":{"observed_at":"2026-08-07T01:07:34.762867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.329767Z","title":"The expressive power of graph neural networks","venue":null,"work_id":"d2633b45-48a2-4711-b308-c113cde145bc","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.766398Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:a9735aa7e50e5a6ed301cdc2a2debae560f7889ce204ee83e0de9a340e78b9ee","observation_id":"a8784a43-4769-4b7c-b9aa-3f204d84f989","resolution":{"observed_at":"2026-08-07T01:07:35.333646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.09893","last_updated":"2022-02-17T20:19:28Z","snapshot_observed_at":"2026-08-10T11:11:37.442332Z","submitted_at":"2019-12-20T15:40:50Z","title":"A Fair Comparison of Graph Neural Networks for Graph Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.09893","snapshot_observed_at":"2026-08-07T01:07:34.769782Z","title":"A fair comparison of graph neural networks for graph classification","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.769782Z"},"links":{"cited_paper":"/paper/1912.09893","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:5bde0de1ce03f745fb65a4abd4882150f2c9bbc244d388f45d29cabcfe8cd1dc","observation_id":"7d9c1a62-20b2-442e-874f-2ed686232905","resolution":{"observed_at":"2026-08-07T01:07:34.769782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.318125Z","title":"Hamilton, Rex Ying, and Jure Leskovec","venue":null,"work_id":"5f200941-c6d4-4edd-adf3-f02fbfb9407b","year":2017},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.773175Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:ed7523c4f87a0b6b8bb3d2374dd9898f93c4baa3ec71b3b700002b845fc9eef1","observation_id":"1a5f3a90-f51b-4927-995c-24bb09521820","resolution":{"observed_at":"2026-08-07T01:07:35.321671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-08-13T05:06:48.606308Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-07T01:07:34.776357Z","title":"How powerful are graph neural networks? ArXiv, abs/1810.00826, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.776357Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:65cc1643075d5d911cc1b4ab6185f98c9d91c421846db1e1646ef4030104538d","observation_id":"237890e8-72bd-43f1-a577-1d39677f547d","resolution":{"observed_at":"2026-08-07T01:07:34.776357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.306450Z","title":"On positional and structural node features for graph neural networks on non-attributed graphs","venue":null,"work_id":"4d41a8bd-ab19-4c94-b2c4-ab1030b6ca57","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.779675Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:cec5ccfd3d5ce40c5e58ff213228d7f4d2d73b8ae8620aecf98fc099945c3934","observation_id":"6379e9c6-0014-4d42-95c9-eb1bf1963b86","resolution":{"observed_at":"2026-08-07T01:07:35.310225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.293830Z","title":"Tenorio, Madeline Navarro, Santiago Segarra, and Antonio G","venue":null,"work_id":"b1e3d949-1dcd-4eac-8be7-f5b86df3899a","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.782710Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:9e80e8bdf3cf84b9c6b727f780dc2ecbd598c0190ed927eed4e6fe1936ee5cf7","observation_id":"2db17021-af98-44e4-b29c-c5615031e7c5","resolution":{"observed_at":"2026-08-07T01:07:35.297501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.280131Z","title":"Graph convolutional networks for graphs containing missing features","venue":null,"work_id":"c7891f07-4bca-45ca-88b9-bc7ddb5cb337","year":2021},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.786290Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:111dd603b1ec2cba8f336fd2615065c9c10a4551aec079a232296d5d33db182e","observation_id":"d9e047c9-29d5-456c-aca9-9da50756b89f","resolution":{"observed_at":"2026-08-07T01:07:35.284685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.268674Z","title":"Graph neural networks can recover the hidden features solely from the graph structure","venue":null,"work_id":"ae2677b0-1901-4ae4-a80d-54e470fbdea0","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.789723Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d8913225d0787b0e97405cbf9662c7361703512ef93fef72fc1f30bab4fe2710","observation_id":"fb2a673b-cbdf-4ed8-b9a5-33dea1d8cd09","resolution":{"observed_at":"2026-08-07T01:07:35.272142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.257455Z","title":"Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications","venue":null,"work_id":"490c949b-2af8-4d01-878b-03339ab13dec","year":2011},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.793659Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:be45e6db475401eebda6ddbd87c3fa41868f1cff69f8bb38ab0925753e2d72a9","observation_id":"4bfeef22-20d7-4930-b1c3-5fb507d112e9","resolution":{"observed_at":"2026-08-07T01:07:35.261165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.246549Z","title":"Community detection in large hypergraphs","venue":null,"work_id":"9d6b1a51-4d9d-4e8c-9516-0180d4dd1fa1","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.796768Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:7c9b98ba44131c11153508d71a391602e7fd547f93d01c2577edef65903e4f20","observation_id":"654a9d97-af46-4886-b32a-3de25370fa5a","resolution":{"observed_at":"2026-08-07T01:07:35.249993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.234027Z","title":"Broad spectrum structure discovery in large-scale higher-order networks, 2025","venue":null,"work_id":"f37e1a34-b300-4d7c-bb41-1c80cef7fdfb","year":2025},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.799931Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:844e55d7e395f17de5af7c2b47f3e787d095a23754d12f989eac9c6788ccdbc5","observation_id":"cb493edf-1ad7-46a6-958c-403a16d4c5c4","resolution":{"observed_at":"2026-08-07T01:07:35.237911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.222341Z","title":"Link prediction under heterophily: A physics-inspired graph neural network approach","venue":null,"work_id":"5593d1d5-c9a7-489f-9787-95c2cdc60d06","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.803173Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:9fe518060baf013f368abd5c1d910a8d5e7df0503acdcf54159d58a6b0272a1a","observation_id":"047fcd38-4cb0-4288-abd9-3da3a0cd46a8","resolution":{"observed_at":"2026-08-07T01:07:35.225964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.06134","last_updated":"2023-07-21T05:02:21Z","snapshot_observed_at":"2026-08-14T06:24:01.188707Z","submitted_at":"2021-06-11T02:44:00Z","title":"Is Homophily a Necessity for Graph Neural Networks?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06134","snapshot_observed_at":"2026-08-07T01:07:34.806548Z","title":"Is homophily a necessity for graph neural networks? arXiv preprint arXiv:2106.06134, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.806548Z"},"links":{"cited_paper":"/paper/2106.06134","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:1063e2fb5d69e7c9c0b5b6b46237108f5a8ff296ec3a9b707b5f0a528dbbf833","observation_id":"57825331-c390-4312-82de-80d0d304c810","resolution":{"observed_at":"2026-08-07T01:07:34.806548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.210353Z","title":"Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing, 02 2023","venue":null,"work_id":"93ae208c-df1c-4698-b43d-38c67592cf97","year":2023},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.810096Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:80338cd8211b7a6aecb5a409d06cf77e0e738a401bf508452848bb6f73c4e5b4","observation_id":"784595c3-ca32-4946-a563-dc4dfee8587b","resolution":{"observed_at":"2026-08-07T01:07:35.214114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.198317Z","title":null,"venue":null,"work_id":"ba9f6a71-3959-4ee1-85e7-fb7c1d4c6f0a","year":2025},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.813768Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:5495e88f0382ac37508dc930c3354482e4aa729fe51ab7ddec20f69f745505cf","observation_id":"c5a2ae96-3176-4c71-bd6f-bd94dad4db96","resolution":{"observed_at":"2026-08-07T01:07:35.202184Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.186828Z","title":"Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond","venue":null,"work_id":"8c2dcba6-32b4-4cd2-8241-1aa87e0b489a","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.817019Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:809ec0ffcb3e7f71d27b65b040c4b76c1494fca189ad9a5b1da92b4743af50e2","observation_id":"a802fd7d-a0e7-421c-b5dc-29c756c8a108","resolution":{"observed_at":"2026-08-07T01:07:35.190508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.175499Z","title":"Graphlime: Local interpretable model explanations for graph neural networks","venue":null,"work_id":"236f2d24-71b6-4ff2-a6a9-ed60084bff42","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.820830Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:cec276d1f022b404f3d747ce4f15a2465b185a6643ab1dc63e267d004cf711a3","observation_id":"008d7420-ff5c-4f14-bc80-a562dfe8d3c1","resolution":{"observed_at":"2026-08-07T01:07:35.179075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.163256Z","title":"Learning deep representations for graph clustering","venue":null,"work_id":"788ca679-c5e5-4614-aae5-d4e3b4efbbaa","year":2014},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.824033Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:3d381bf9a9313e5408b3872f6c419dd101e18176626162e9e5aa74bb7a2dedf6","observation_id":"2c28ba9e-6a7b-4602-a81e-5f6bb8868384","resolution":{"observed_at":"2026-08-07T01:07:35.167243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.151126Z","title":"Attributed graph clustering: a deep attentional embedding approach","venue":null,"work_id":"e16d0dfe-f26e-4a3d-86ab-cb539d551fa7","year":2019},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.827597Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:af718af20cd4e52265457802f76d4a35061d43ab42208814266a76413efc9b70","observation_id":"12aa1b1e-7ff1-43c1-aee5-bbc839f0c72b","resolution":{"observed_at":"2026-08-07T01:07:35.154805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.138968Z","title":"Deep k-means clustering based on graph neural networks: Leveraging cohesion and separation in graph nodes","venue":null,"work_id":"1b65c829-3ae0-4f26-8c81-206142bffbb3","year":2024},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.831070Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:296994a8da8b26583d39615cffb0e412224dbc0c76481959ad55e78db4a75047","observation_id":"8960613c-f711-44f9-85d8-1d4423d7f738","resolution":{"observed_at":"2026-08-07T01:07:35.143265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.127299Z","title":"Viualizing data using t-sne.Journal of Machine Learning Research, 9:2579–2605, 11 2008","venue":null,"work_id":"66a0643f-c052-4935-8f68-596423ef8578","year":2008},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.834676Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:509f3534ea6b20cf917c3f24c0fa3bc0330705bd99a309601372519a9bc91070","observation_id":"e829f122-6941-41d8-b494-d1af234ddff3","resolution":{"observed_at":"2026-08-07T01:07:35.131063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.115317Z","title":"Flexible inference in heterogeneous and attributed multilayer networks","venue":null,"work_id":"8253c769-610c-4df6-b594-d691504b96f0","year":2025},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.837958Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:7ac99e380ac0d78eba9ec406b6c77b3abd7b6e50d448f0f6b6d7adc8a47de16d","observation_id":"254df6ab-1fe4-4b24-be16-a940153c1623","resolution":{"observed_at":"2026-08-07T01:07:35.119240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:07:35.103192Z","title":"How powerful are spectral graph neural networks","venue":null,"work_id":"8042da39-74c2-47ac-82b3-d9c25a788cf1","year":2022},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.841472Z"},"links":{"citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:68fb7b795a25b5003956f7a708cc500429b7c66203ae954fba45ce2d42231c88","observation_id":"119466d0-2647-4a71-8caf-9f2c105c6ca5","resolution":{"observed_at":"2026-08-07T01:07:35.106829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00452","last_updated":"2020-09-22T01:08:22Z","snapshot_observed_at":"2026-08-07T20:20:19.535043Z","submitted_at":"2019-10-01T14:37:22Z","title":"On the Equivalence between Positional Node Embeddings and Structural Graph Representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00452","snapshot_observed_at":"2026-08-07T01:07:34.844695Z","title":"On the equivalence between positional node embeddings and structural graph representations","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?","version":3},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T01:07:34.844695Z"},"links":{"cited_paper":"/paper/1910.00452","citing_paper":"/paper/2506.11869"},"observation_digest":"sha256:d1bdd92e9305be806125812be6bac21b245dd941d6ee2c13a4e910690138f07b","observation_id":"58ae4db2-bd59-4591-8a2d-ac75872df819","resolution":{"observed_at":"2026-08-07T01:07:34.844695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.11869","last_updated":"2025-08-22T19:08:50Z","latest_version":3,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-13T22:44:52.544831Z","submitted_at":"2025-06-13T15:19:28Z","title":"How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":2,"verified_fuzzy":60},"total_outbound_references":75},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2506.11869."}