{"as_of":"2026-08-20T17:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:17c2dc0ce5c97b331ec2f2c36e06762f31eb845b892c83a44b224a1045b1e10f","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-10T20:22:48.051821Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2607.06833/citation-record","integrity":"/paper/2607.06833/integrity","json":"/paper/2607.06833/citation-record.json","paper":"/paper/2607.06833"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T20:27:36.979717Z","title":"Graph signal generative diffusion 12 models,","venue":null,"work_id":"623f9d1d-6bdf-4b8e-9509-694a4ef9cce4","year":2026},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:b8f6fd682d9eeee1abee5d1b832cf9b9981a705b62cfd0648928f7da37815dff","observation_id":"3157d157-bfbd-492b-9967-f9711d2895f5","resolution":{"observed_at":"2026-07-10T20:27:36.980960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.05175","last_updated":"2026-07-28T19:06:16Z","snapshot_observed_at":"2026-08-15T16:17:38.777226Z","submitted_at":"2026-04-06T21:12:25Z","title":"Graph Signal Diffusion Models for Wireless Resource Allocation","version":2},"cited_work":{"arxiv_id":"2604.05175","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.05175","snapshot_observed_at":"2026-07-10T20:27:36.623702Z","title":"Graph Signal Diffusion Models for Wireless Resource Allocation","venue":"eess.SP","work_id":"0a679a50-06ff-4e78-b930-8fe2a83d7518","year":2026},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"cited_paper":"/paper/2604.05175","citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:fee488ca73af5193167826f9f3257e89f408ee4ff7c965661808a1d65ff3742b","observation_id":"1e3f1c7f-3a31-4e73-a6ab-ddc27c631224","resolution":{"observed_at":"2026-07-10T20:27:36.625083Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.957694Z","title":"Neural graph collaborative filtering,","venue":null,"work_id":"15e73d1b-eae1-4f96-bd84-b8ec7cd28319","year":2019},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:9d8134f1bda56f73227e510f87baed6fc939139d27e79132518c24967d70bd3b","observation_id":"062541bf-b7a9-4016-aaa0-244998cb1085","resolution":{"observed_at":"2026-07-10T20:27:36.958831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.969003Z","title":"Ultragcn: Ultra simplification of graph convolutional networks for recommendation,","venue":null,"work_id":"8212d770-f364-4197-8df2-344331841d75","year":2021},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:7ab482e26a08ff55b08ff16c2ef4040dbc0beec68196bd1d84f488112f20c437","observation_id":"677c0395-cc1b-44c2-a692-5ad72c73a526","resolution":{"observed_at":"2026-07-10T20:27:36.970208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.974471Z","title":"Personalized graph signal processing for collaborative filtering,","venue":null,"work_id":"b723f593-0116-4c56-9057-8bb1faa0b157","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:bc8579bb73927c5496e68317d7cbac71e452ca01ab181d54571ff5cd66438b6d","observation_id":"a3c1b172-43e0-4310-b9ea-2fddaf6b7d6b","resolution":{"observed_at":"2026-07-10T20:27:36.975640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.935148Z","title":"Optimal wireless resource allo- cation with random edge graph neural networks,","venue":null,"work_id":"3f8e6100-1ec5-40f1-a076-d7fa6132ac0e","year":2020},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:0ded1745ca6fb4c852bf2640f493613a7a7c149f2a574f89594b904e0e981f6f","observation_id":"a1c1c203-538f-4454-9f64-808af472c44a","resolution":{"observed_at":"2026-07-10T20:27:36.936342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.937029Z","title":"Graph neural networks for wireless communications: From theory to practice,","venue":null,"work_id":"338c416b-53b0-4a84-988b-17169ebd27f2","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:a1085ed20ee8cf66c4d42f916b7225d9ea7dbd67d79a57357958d0a8cf7f9e15","observation_id":"56ba35c2-8825-4806-b324-8f1122afb1b3","resolution":{"observed_at":"2026-07-10T20:27:36.938177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.972552Z","title":"Link scheduling using graph neural networks,","venue":null,"work_id":"ccafe715-6182-40b1-8ff0-59e836234ccf","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:208dafbf45e14606e3a6d88741f77cca4febb08b6ff6855d4f6a206302460c77","observation_id":"18d06fd8-1b10-416c-95b3-df021c378992","resolution":{"observed_at":"2026-07-10T20:27:36.973720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.970730Z","title":"ENGNN: A general edge-update empowered GNN architecture for radio resource management in wireless networks,","venue":null,"work_id":"c809cd67-64bd-4074-9b85-6713e63785a3","year":2024},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:fdbbd640e080f4a388bc9bfa23942da89a693e86b1718607eccce2f9e7ffb3d2","observation_id":"8b24fe3f-2c78-4eee-a4a3-0b9d624d618f","resolution":{"observed_at":"2026-07-10T20:27:36.971990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.959360Z","title":"Deep graph unfolding for beamforming in mu-mimo inter- ference networks,","venue":null,"work_id":"00fb6e31-f467-402e-99b3-b32682725eb1","year":2024},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:7ff2d31baee993bb434e0dec2341de545165901f387991d5422267d4f2358a44","observation_id":"4372877a-b18f-49b6-80c2-b78050b235d0","resolution":{"observed_at":"2026-07-10T20:27:36.960945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.976159Z","title":"Fast state-augmented learning for wireless resource allocation with dual variable regression,","venue":null,"work_id":"34e6424f-2b2b-4810-91a7-8778c1980b47","year":2025},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:91941ba766830a9023a728b2b915cb3a7cdfe6c17175aa04ddada928370215ca","observation_id":"a099f4f2-9540-4c61-b664-0a8b33261636","resolution":{"observed_at":"2026-07-10T20:27:36.977324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.933427Z","title":"Incorporating corporation relationship via graph convolutional neural networks for stock price prediction,","venue":null,"work_id":"3b199270-b6ae-4d3d-b12a-30c3bd50438e","year":2018},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:286df0a4e408f8f06dfbd6e17bf179f4ec381aae11df6db01b4822eb449a1e33","observation_id":"f5411815-67b7-4711-b363-bd014ddb275b","resolution":{"observed_at":"2026-07-10T20:27:36.934601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.977828Z","title":"Spatiotemporal hypergraph convolution network for stock movement forecasting,","venue":null,"work_id":"710e788f-593e-4bbc-a78b-ddc4762f95f5","year":2020},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:311472d6c1cc804f4e7fd6168e2401a4955df1b396878a261ecaa91590de7724","observation_id":"043339f8-2830-4b6b-858e-ee5272d21973","resolution":{"observed_at":"2026-07-10T20:27:36.979032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.930035Z","title":"Attention based dynamic graph neural network for asset pricing,","venue":null,"work_id":"f8c91c2c-1dbc-486f-9ac5-40343b6d6a1f","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:4a5b3989c8fe84093d3d9e3ab2f52a18d6794ae04249156932f1d857e2b68785","observation_id":"ee63f6b1-ca5d-459a-9d1d-b975c61f2c43","resolution":{"observed_at":"2026-07-10T20:27:36.931162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.945298Z","title":"Stationary signal processing on graphs,","venue":null,"work_id":"f8f0a533-eb6d-4f84-835c-76f5ff2bb5b4","year":2017},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:a119b2ae080273220b2e5a5e2536ea17bc68acc297eb2b9d4285343d82d091c3","observation_id":"2c9c937e-5867-4f10-833c-f6e1aca0f6aa","resolution":{"observed_at":"2026-07-10T20:27:36.946428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.921481Z","title":"Score-based generative modeling of graphs via the system of stochastic differential equations,","venue":null,"work_id":"ddb6609c-6e01-4b84-8b7a-84561dbe3f44","year":2022},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:0271d72bf89a4952c43f3a30f4da49c9f35af403a379a5b3bf5de16a43fa8590","observation_id":"1d817e66-c5d1-46c1-a6b3-92447fe79254","resolution":{"observed_at":"2026-07-10T20:27:36.922649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.924838Z","title":"DiGress: Discrete denoising diffusion for graph generation,","venue":null,"work_id":"1328a0f9-0d75-42ba-bfcf-9004178c1f08","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:74e23dd781fcd70aa6107655d0c480a6994c8dc98a715741f24d28544955557c","observation_id":"c7cf5ddc-f01c-4f9c-aac7-8f5a8a1878c4","resolution":{"observed_at":"2026-07-10T20:27:36.925969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.948694Z","title":"Equivariant diffusion for molecule generation in 3d,","venue":null,"work_id":"06dc4e88-d80b-4022-bc01-7cc601ecb40c","year":2022},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:f0857917d55c25b6150cf96f1fd3e6ff49e928706bd8084df1cfb4dd5776ea42","observation_id":"2ff0987c-c587-4975-95d8-1e0f3762f85f","resolution":{"observed_at":"2026-07-10T20:27:36.949756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.918130Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":"ab183ad4-038f-4652-b69b-3b278565f258","year":2014},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:756a533fa58a7246c0d18d7a5b9f65677ff73c004bd431ed8f5cd3f901a3b39f","observation_id":"9dc43e9f-da0c-4d23-85fb-34755148603f","resolution":{"observed_at":"2026-07-10T20:27:36.919281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.919834Z","title":"Generative adversarial nets,","venue":null,"work_id":"486a5b09-65a6-40b9-b408-a482d8cc79c4","year":2014},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:77ab6c6324c245580bfb564a8bd52e78317483894e67c6e8c45449df69cd7989","observation_id":"290ca8f2-ae01-4f74-adc9-e368b73b6fbc","resolution":{"observed_at":"2026-07-10T20:27:36.920876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.941967Z","title":"Variational inference with normalizing flows,","venue":null,"work_id":"dff25a04-66f3-4712-a7cf-660bbfa46804","year":2015},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:522d5385f7ca7af70aa8f66f2d6e66a1781d649bbf81997afd0092c3723bb338","observation_id":"5486ef4c-ef7e-4661-99e5-223653038f0b","resolution":{"observed_at":"2026-07-10T20:27:36.943092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.943635Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":"48b70137-fbff-4e96-b176-8e80a297dcff","year":2020},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:a8b105cb3b62e68328f2ed12dd54b1ece74f970acd5104e8c3e9a49a7693d0c5","observation_id":"b4fba616-1442-4959-80a5-7e24118b253b","resolution":{"observed_at":"2026-07-10T20:27:36.944746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.983366Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":"634dad82-2957-4655-89d7-db1bded1f04e","year":2021},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:2faf870ac08f950abc7ed1490c13075e64a7e3454063939fdb962b03107ecc7a","observation_id":"a4f3a390-e642-40d2-b3f9-5fd27a38cf0b","resolution":{"observed_at":"2026-07-10T20:27:36.985558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.916270Z","title":"Score-based generative modeling through stochastic differential equations,","venue":null,"work_id":"cbc19285-a18f-414c-bf79-f327e0202851","year":2021},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:5b9055f7b5e642dd9389218575b984b94f60b58d413325668b0abc17d86f2f52","observation_id":"17668259-3549-4742-acff-75d517fb4da1","resolution":{"observed_at":"2026-07-10T20:27:36.917461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.938685Z","title":"Flow matching for generative modeling,","venue":null,"work_id":"800deac2-3246-4de6-b938-15a1b9766e29","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:6998380c01c236879e13416e435034cb446296acb6d2b0a1d13af2c6082a560f","observation_id":"aa1eb3a8-63af-4c21-8bf7-0ef16f72d51f","resolution":{"observed_at":"2026-07-10T20:27:36.939778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.912647Z","title":"DiffSTG: Probabilistic spatio- temporal graph forecasting with denoising diffusion models,","venue":null,"work_id":"a9b10eb5-f0a6-46f8-8b05-9e316d466d34","year":2023},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:9e44397d5ba77b1feb57dee40b7c5b42cf87d8beab55236e14bdb1ac47801389","observation_id":"717171ce-d6d5-4954-97f1-0a9ea463ff6e","resolution":{"observed_at":"2026-07-10T20:27:36.913965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.914531Z","title":"DiffSTOCK: Probabilistic relational stock market predictions using diffusion models,","venue":null,"work_id":"02d9ff75-24a7-4a4f-9d7c-52c521065668","year":2024},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:59a1450444fa23f5d25da77285085d2081bd05d70904bc88389ddb142284f0df","observation_id":"2cad116d-994b-4ded-afb8-c6c7a8d56fd3","resolution":{"observed_at":"2026-07-10T20:27:36.915694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.986097Z","title":"DHMoE: Diffusion generated hierar- chical multi-granular expertise for stock prediction,","venue":null,"work_id":"45c0bf0b-c580-45ae-9aa4-470cb2ab4613","year":2025},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:33cfef6e07d07407a4ed55d15e640e6749c78a0c56f2c666067212b514f97943","observation_id":"2601fc95-3d87-48a7-85d7-3ece21938c2e","resolution":{"observed_at":"2026-07-10T20:27:36.987254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.940297Z","title":"Graph-aware diffusion for signal generation,","venue":null,"work_id":"782e5ac4-265f-46c3-bf73-2d335c39325d","year":2026},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:71718c4674336abd2d9d26bd72e76a745e62cba95ddf375e2a6383f618d1695a","observation_id":"01a0cc31-8b7c-4312-a67c-600a06130a8c","resolution":{"observed_at":"2026-07-10T20:27:36.941427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.951945Z","title":"Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems,","venue":null,"work_id":"38b2fb91-6dac-47c0-badd-1485f4fefb10","year":2024},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:35561e879dc461e3c2339d2bfdeaac78d7fc1587a0873c76ec352e16edab24ad","observation_id":"4c04f42f-29ff-415c-8538-d4b3f496c910","resolution":{"observed_at":"2026-07-10T20:27:36.955366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.950260Z","title":"Diffsg: A generative solver for network optimization with diffusion model,","venue":null,"work_id":"a630fd20-6296-4c73-9d72-039172c8885c","year":2025},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:c9757aa88a673a0d2c58bd301f33d26f96805013974c1fd2c1c73543e5e059a8","observation_id":"c9a014de-1989-4444-b52c-d41b9da351ce","resolution":{"observed_at":"2026-07-10T20:27:36.951390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.907457Z","title":"Generative diffusion models for resource allocation in wireless networks,","venue":null,"work_id":"a66acc52-08a7-454a-b6a9-a5885dc34cfc","year":2025},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:679e2afd7c127dcbf89c42219a46db295a81f7ca9335ec8276cc38b7c494819c","observation_id":"cc6b78d6-de6e-4b9d-bfd8-01cbc7674731","resolution":{"observed_at":"2026-07-10T20:27:36.908698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.909259Z","title":"Diffu- sion model for multiple antenna communication,","venue":null,"work_id":"df477850-927d-4755-99a9-0aa421157a02","year":2025},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:7a027e4661953ba7c47a4f67b6584c14691aef57a3de57fa040b9fcae955fa2c","observation_id":"8bc093ae-d5ca-4d6c-a9c6-74b1061e6439","resolution":{"observed_at":"2026-07-10T20:27:36.910403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.910948Z","title":"Deterministic score-based diffusion model for channel estimation in ris-assisted mimo systems,","venue":null,"work_id":"77f48b7f-6b44-41b1-b42b-c54a62d4756c","year":2025},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:49c7111fa71eb04e8f453cd4e064f39ebb3f11b45a57786c34ae86495b8bdc33","observation_id":"a4e86f09-a4ad-47fd-99cf-e198c61b48cc","resolution":{"observed_at":"2026-07-10T20:27:36.912109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.946950Z","title":"Gen- erating high dimensional user-specific wireless channels using diffusion models,","venue":null,"work_id":"9ebbf7c3-6e9f-499a-832f-7ea9b899b0b6","year":2026},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:a183b4b7d9ba229469e7996d5a66f814ed437ea937cc90f37b5c36e65069a019","observation_id":"818aabfc-e90a-4519-adfa-a84827ed0c93","resolution":{"observed_at":"2026-07-10T20:27:36.948163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.923170Z","title":"U-net: Convolu- tional networks for biomedical image segmentation,","venue":null,"work_id":"5eafb495-2bad-4deb-9f35-f7ccdea2c49a","year":2015},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:928e3dd7d9f4d38ce4595b8db91a090680d12d934077a38df2d6d990b84b98b2","observation_id":"1a262ab7-89d7-4069-832b-5ceee7acc009","resolution":{"observed_at":"2026-07-10T20:27:36.924300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.963457Z","title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,","venue":null,"work_id":"6799ba33-7aed-4a0c-8ae2-e3e552edad25","year":2021},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:fc6e68ae61903f367a6a0a9a0ec8c7efb5a66dd47c40da6b36a1b93a18d41cfb","observation_id":"6703c5b1-4d2e-4bbf-80d2-4b00b02db856","resolution":{"observed_at":"2026-07-10T20:27:36.964678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.981524Z","title":"Convolutional neural network architectures for signals sup- ported on graphs,","venue":null,"work_id":"323ad8b1-4b49-4bf9-80ff-91386878b8a0","year":2019},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:0177c04a71574d1f94e928fcc91bf3e8a6e0d903afb0ecc9efb22d6c6161ba1a","observation_id":"822e9ac1-ec52-42ed-80ae-718bcaa1a29d","resolution":{"observed_at":"2026-07-10T20:27:36.982777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.965225Z","title":"Hierarchical graph representation learn- ing with differentiable pooling,","venue":null,"work_id":"419b3819-bc2b-41f7-a4ae-2e09cae05bf8","year":2018},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:1a529f7009bed36835b56c4b341dda20062163d52e300775af15f55fb9ba6939","observation_id":"b3f44bc6-b421-4493-8432-ab5183a76906","resolution":{"observed_at":"2026-07-10T20:27:36.966682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.955933Z","title":"Graph u-nets,","venue":null,"work_id":"c64fa7d1-565c-4d8f-9362-de4b521844e6","year":2019},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:d4e40709d92237c5fd664ea7120bb7c976f3c6a1d142d780c3b830367cb6bc45","observation_id":"db9673e0-f690-4924-a5d0-02f1de944128","resolution":{"observed_at":"2026-07-10T20:27:36.957145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.961588Z","title":"Striving for simplicity: The all convolutional net,","venue":null,"work_id":"a45a0604-3236-416c-9ebb-c553af5686e4","year":2015},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:c907bfe6401351be5af16f60a47ed404a9724e7ea7ea21445c08e55f1c15183a","observation_id":"037a8998-49a6-4f61-9493-9527038d8365","resolution":{"observed_at":"2026-07-10T20:27:36.962926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.967227Z","title":"Multi-scale context aggregation by dilated convolutions,","venue":null,"work_id":"5a794dfc-3c79-4675-87db-60468a578ae9","year":2016},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:f2183dd4acd08f55b455497a45eb9f39f1edacb0b24cbc4db1f068d3876d75d7","observation_id":"08d9e394-090b-458a-aaa2-85717907d416","resolution":{"observed_at":"2026-07-10T20:27:36.968469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.931731Z","title":"Graph neural networks: Architectures, stability, and transferability,","venue":null,"work_id":"dc8e4fe0-734a-493f-9eed-ff0490f0baa1","year":2021},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:4a759b88bb0a3ad2d09b8b3254ec77908d9e4b065498443f8f353c9d43d0423e","observation_id":"8bab9202-bf09-445d-95db-1a9b3cc1f44f","resolution":{"observed_at":"2026-07-10T20:27:36.932883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":"1308.3432","doi":"10.48550/arxiv.1308.3432","metadata_source":"pith","pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","venue":"cs.LG","work_id":"1fe8c7c8-aff7-4b94-9096-e549d7e60789","year":2013},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:e3e74d0f3c425a1120010eb737ce884e934346a634a92d53a6c5dcbffa89aeff","observation_id":"794ed825-86b2-420e-ac23-b9c751bcfe8e","resolution":{"observed_at":"2026-07-10T20:27:36.621059Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-05-19T16:22:27.235199+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T16:22:27.235199+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.928295Z","title":"Reversible instance normalization for accurate time- series forecasting against distribution shift,","venue":null,"work_id":"013c25d2-fc29-49a5-885c-d591e6f4cd10","year":2022},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:76d6a54ff7fb6e04a1a8e6a24bb1b9d673f9087cb0abc8478a74ac0545cba49c","observation_id":"4a81e317-bd77-45bf-8ea3-745d616a2671","resolution":{"observed_at":"2026-07-10T20:27:36.929492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-07-10T20:27:36.926546Z","title":"Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement,","venue":null,"work_id":"a2d1286f-d052-469b-96de-c70ac85ab802","year":2019},"citing_paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-10T20:22:48.051821Z"},"links":{"citing_paper":"/paper/2607.06833"},"observation_digest":"sha256:10aa407ced99d6daa852781ab79b6af597a26bb6f899018094923258c9ade13b","observation_id":"212a7ca9-17a4-4fe7-af3c-9c0412378ccd","resolution":{"observed_at":"2026-07-10T20:27:36.927748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.06833","last_updated":"2026-07-07T22:02:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T16:57:27.578465Z","submitted_at":"2026-07-07T22:02:13Z","title":"Generative Diffusion Models of Stochastic Graph Signals"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":2,"verified_fuzzy":44},"total_outbound_references":46},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.06833."}