{"as_of":"2026-08-09T19:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:613ceda44c8918d89472b67905255862b10f9884e0ab2304011c6da6aca9f7e2","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":51,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T18:05:40.997527Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T20:08:56.312991Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2104.13478","last_updated":"2021-05-02T16:16:03Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-04-27T21:09:51Z","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","version":2},"reference_index":108,"source":"pdf_text","source_observed_at":"2026-05-13T02:39:29.411021Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2104.13478"},"observation_digest":"sha256:3931afd741e1bd9e751bf353e902d06177db96722c42cd59be51097273dd50ae","observation_id":"822dcdec-7c8f-4451-a720-cc1e36d4fb76","resolution":{"observed_at":"2026-05-13T02:39:29.755378Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2407.07639","last_updated":"2026-05-11T14:21:15Z","snapshot_observed_at":"2026-08-02T16:10:53.081724Z","submitted_at":"2024-07-10T13:20:47Z","title":"Explaining Graph Neural Networks for Node Similarity on Graphs","version":2},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-05-23T23:00:49.929298Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2407.07639"},"observation_digest":"sha256:41caaf71e3675fb5c515d73a9c8fbd911dcb550f962a89c3c847e284900466c7","observation_id":"a9f2bd62-bc11-40b1-8c82-7e3573aff7ca","resolution":{"observed_at":"2026-05-23T23:03:34.510579Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2408.13471","last_updated":"2026-05-07T12:57:54Z","snapshot_observed_at":"2026-08-02T21:34:49.925481Z","submitted_at":"2024-08-24T05:13:02Z","title":"Disentangled Generative Graph Representation Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-23T21:23:33.474134Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2408.13471"},"observation_digest":"sha256:42d5ebf243d37f228fbd89a940a3c7b83bc8369cd29d597f447ee6f5d45fb013","observation_id":"0ed064ae-656b-4d90-ad3e-30fbaeefbb6b","resolution":{"observed_at":"2026-05-23T21:25:51.594575Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2501.00773","last_updated":"2026-04-09T14:25:52Z","snapshot_observed_at":"2026-08-02T06:24:28.654744Z","submitted_at":"2025-01-01T08:48:53Z","title":"OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks","version":3},"reference_index":119,"source":"pdf_text","source_observed_at":"2026-05-23T06:10:49.711018Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2501.00773"},"observation_digest":"sha256:ddb6e22e18194c9b5ddf6a722afaf74cdf1847949cd5b43989db813e8ee1fa93","observation_id":"b3638ba3-e74a-4af0-bcdb-1138f92e693d","resolution":{"observed_at":"2026-05-23T06:12:38.357524Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-09T18:05:40.997527Z","title":"Deep graph contrastive representation learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.01684","last_updated":"2025-09-03T12:25:24Z","snapshot_observed_at":"2026-08-09T17:58:18.423783Z","submitted_at":"2025-02-02T07:42:45Z","title":"Predict, Cluster, Refine: A Joint Embedding Predictive Self-Supervised Framework for Graph Representation Learning","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T18:05:40.997527Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2502.01684"},"observation_digest":"sha256:6bfee443e74da01d5c4645679767924343c089c8c92a3b42433fd249e99fbeda","observation_id":"002abdbf-0e40-4f8a-979e-c6bec92a0d89","resolution":{"observed_at":"2026-08-09T18:05:40.997527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T15:29:50.727357Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15103","last_updated":"2025-08-08T06:14:32Z","snapshot_observed_at":"2026-08-09T04:22:03.591072Z","submitted_at":"2025-05-21T04:54:18Z","title":"Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.727357Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2505.15103"},"observation_digest":"sha256:661bea79af3de77ab30543e35d43ff25ccfe80f8c988996104a28fdd834775a7","observation_id":"cfe27ec2-5287-4c45-a590-1e7ccfff573e","resolution":{"observed_at":"2026-08-07T15:29:50.727357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T14:24:33.508442Z","title":"Deep graph contrastive representation learning.ArXiv preprint arXiv:2006.04131, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.19020","last_updated":"2025-05-25T07:56:56Z","snapshot_observed_at":"2026-08-09T07:14:35.075567Z","submitted_at":"2025-05-25T07:56:56Z","title":"HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:24:33.508442Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2505.19020"},"observation_digest":"sha256:dc0136e7204ed60f569fafac81c3f248e7f69007455f0b6b8f77719f8b9f3547","observation_id":"a569c23b-4002-4662-9299-e79467170a79","resolution":{"observed_at":"2026-08-07T14:24:33.508442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T10:30:12.712612Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05214","last_updated":"2025-06-05T16:28:12Z","snapshot_observed_at":"2026-08-07T10:20:11.830589Z","submitted_at":"2025-06-05T16:28:12Z","title":"Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T10:30:12.712612Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2506.05214"},"observation_digest":"sha256:8689acec9e605d91e9c2f209ed0c839e99c23d8c8e62e21b1b0459cd844b9753","observation_id":"1e973800-ae0d-4705-8744-c34004e1c616","resolution":{"observed_at":"2026-08-07T10:30:12.712612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T05:17:17.444369Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08326","last_updated":"2025-06-10T01:27:19Z","snapshot_observed_at":"2026-08-09T14:48:30.926093Z","submitted_at":"2025-06-10T01:27:19Z","title":"Graph Prompting for Graph Learning Models: Recent Advances and Future Directions","version":1},"reference_index":147,"source":"pdf_text","source_observed_at":"2026-08-07T05:17:17.444369Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2506.08326"},"observation_digest":"sha256:4876f0cbbe28bd1d75b990c2ee285b6385d78eb98ba6e77633c52869b16c13ca","observation_id":"36a863cf-0320-4155-b57e-87d877982b9a","resolution":{"observed_at":"2026-08-07T05:17:17.444369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T00:42:58.213760Z","title":"Deep graph contrastive representation learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.13178","last_updated":"2025-06-16T07:43:18Z","snapshot_observed_at":"2026-08-09T02:30:57.581405Z","submitted_at":"2025-06-16T07:43:18Z","title":"Enhancing Large Language Models with Reliable Knowledge Graphs","version":1},"reference_index":134,"source":"arxiv_source","source_observed_at":"2026-08-07T00:42:58.213760Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2506.13178"},"observation_digest":"sha256:025b564c2067b37ed18f74d1b453ad5ee5e2e5a188e030ab67d06707dd6bd6d5","observation_id":"d85256be-fb58-4cbd-8fae-976f3020c2e4","resolution":{"observed_at":"2026-08-07T00:42:58.213760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T00:26:20.955313Z","title":"Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.14114","last_updated":"2025-06-17T02:12:19Z","snapshot_observed_at":"2026-08-08T21:03:30.013618Z","submitted_at":"2025-06-17T02:12:19Z","title":"Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T00:26:20.955313Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2506.14114"},"observation_digest":"sha256:46b92f966ed695a28e11d1d5cc2b37a3f4a19f21c50cc88bce391358435c6a1c","observation_id":"6f52ff62-3ec2-4bc0-b756-5d5466b33e04","resolution":{"observed_at":"2026-08-07T00:26:20.955313Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-07T13:17:30.614825Z","title":"Deep graph contrastive representation learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.15698","last_updated":"2025-06-23T07:46:50Z","snapshot_observed_at":"2026-08-09T19:43:01.938108Z","submitted_at":"2025-05-28T13:47:50Z","title":"Global Context-aware Representation Learning for Spatially Resolved Transcriptomics","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:17:30.614825Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2506.15698"},"observation_digest":"sha256:bf73a482dd072cbcc5120fee600de9d68ab4cbfc47f1effca3d07bb4398d4960","observation_id":"29ebae98-0344-4381-88ab-dd14793c529b","resolution":{"observed_at":"2026-08-07T13:17:30.614825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-06T22:16:20.174075Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22299","last_updated":"2025-06-27T15:11:49Z","snapshot_observed_at":"2026-08-08T14:37:49.798521Z","submitted_at":"2025-06-27T15:11:49Z","title":"CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T22:16:20.174075Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2506.22299"},"observation_digest":"sha256:d60b1cec6f78202c01a86753bb51d762be5ab435853e9925843c23088a781602","observation_id":"9d4e8b60-6fa7-4f16-81be-e26d17a41e9a","resolution":{"observed_at":"2026-08-06T22:16:20.174075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-06T19:38:35.493904Z","title":"Deep Graph Contrastive Representation Learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05086","last_updated":"2025-07-07T15:10:03Z","snapshot_observed_at":"2026-08-09T07:14:35.570138Z","submitted_at":"2025-07-07T15:10:03Z","title":"Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:38:35.493904Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2507.05086"},"observation_digest":"sha256:798a203f7f2ea81dab822e6f741172b2213fffb0142b353e8c16fafc2a5ea316","observation_id":"f56624ee-391a-4ce8-8b6f-d13153d5aec3","resolution":{"observed_at":"2026-08-06T19:38:35.493904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-06T19:04:40.184562Z","title":"Deep graph contrastive representation learning,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.06541","last_updated":"2025-07-09T04:52:15Z","snapshot_observed_at":"2026-08-09T07:13:52.723172Z","submitted_at":"2025-07-09T04:52:15Z","title":"Graph-based Fake Account Detection: A Survey","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:04:40.184562Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2507.06541"},"observation_digest":"sha256:d6b73dfbc54a8f169ef6fef83b1254a69fb669b4d11cf25d13dc1964f90e270b","observation_id":"2d747b74-f47f-498c-9fe9-6272cfdde94f","resolution":{"observed_at":"2026-08-06T19:04:40.184562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-06T19:06:40.075344Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.07141","last_updated":"2025-07-09T03:41:48Z","snapshot_observed_at":"2026-08-09T07:14:36.064181Z","submitted_at":"2025-07-09T03:41:48Z","title":"Str-GCL: Structural Commonsense Driven Graph Contrastive Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:40.075344Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2507.07141"},"observation_digest":"sha256:7a4407d53a13265d9579d9a15e1eaf55eac27ff7176927c0e03b73515bad14da","observation_id":"146b50de-3460-4d8f-889b-44808996c805","resolution":{"observed_at":"2026-08-06T19:06:40.075344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-06T18:59:23.894061Z","title":"Deep graph contrastive representation learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.13368","last_updated":"2025-08-05T09:24:58Z","snapshot_observed_at":"2026-08-09T07:14:45.493033Z","submitted_at":"2025-07-09T13:42:43Z","title":"Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-06T18:59:23.894061Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2507.13368"},"observation_digest":"sha256:5f5d6b66d0a57f950dcc258852b1e722878105b76c5508bfddfcf170b9317720","observation_id":"6ca626d8-8d4a-46d8-9cff-222dc2674d60","resolution":{"observed_at":"2026-08-06T18:59:23.894061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-06T16:24:08.806495Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.13765","last_updated":"2025-07-18T09:17:04Z","snapshot_observed_at":"2026-08-08T06:44:13.884566Z","submitted_at":"2025-07-18T09:17:04Z","title":"Dual-Center Graph Clustering with Neighbor Distribution","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:24:08.806495Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2507.13765"},"observation_digest":"sha256:fa6338ace2ed0d117159d6f8905805d1ee883ca2c6e1903010ce84321af0d6dc","observation_id":"f5db729a-f5f2-43c9-8ff8-dbc4be9ea102","resolution":{"observed_at":"2026-08-06T16:24:08.806495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-05T18:23:07.016717Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.14808","last_updated":"2025-08-20T15:58:01Z","snapshot_observed_at":"2026-08-09T12:02:57.189234Z","submitted_at":"2025-08-20T15:58:01Z","title":"Enhancing Contrastive Link Prediction With Edge Balancing Augmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T18:23:07.016717Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2508.14808"},"observation_digest":"sha256:22c48896664694b0daa37a50c85f74f6a7ab2d3a34db0b8aa8d62ba2220c98cf","observation_id":"2cb7dd8a-9ee8-4454-b18a-624e4d559d9a","resolution":{"observed_at":"2026-08-05T18:23:07.016717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-05T14:36:36.476962Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.06975","last_updated":"2025-08-28T19:13:10Z","snapshot_observed_at":"2026-08-07T21:39:28.054869Z","submitted_at":"2025-08-28T19:13:10Z","title":"GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T14:36:36.476962Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2509.06975"},"observation_digest":"sha256:6efff3daa76303916237c6a822edcc92403720960030fe73d9af519f62c17aa2","observation_id":"9ba7fc71-b303-4c0e-80ac-d745b48b6d71","resolution":{"observed_at":"2026-08-05T14:36:36.476962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-04T09:18:29.113304Z","title":"Deep graph contrastive representation learning,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2510.16311","last_updated":"2026-06-18T15:18:42Z","snapshot_observed_at":"2026-08-09T07:13:02.361179Z","submitted_at":"2025-10-18T02:46:53Z","title":"Toward General Digraph Contrastive Learning: A Dual Spatial Perspective","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T09:18:29.113304Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2510.16311"},"observation_digest":"sha256:d82b5eb2fa36c8d4b94e97a88dd2f944876a6ebd5128dfd711b482c488785d2f","observation_id":"6541e802-e851-4c89-8766-2bcf0d2c5200","resolution":{"observed_at":"2026-08-04T09:18:29.113304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2510.22555","last_updated":"2026-05-05T03:00:23Z","snapshot_observed_at":"2026-08-04T23:07:25.119627Z","submitted_at":"2025-10-26T07:10:07Z","title":"Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-18T04:51:23.281272Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2510.22555"},"observation_digest":"sha256:2ade79729e7f94183020acb28653b6429d089bee750ecf35f3ba8f399c764b37","observation_id":"1694faf4-dda6-4425-a9b2-3d0102bb6192","resolution":{"observed_at":"2026-05-18T04:52:23.393656Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-04T07:55:01.225653Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2510.23469","last_updated":"2026-06-02T15:31:54Z","snapshot_observed_at":"2026-08-08T07:18:21.382631Z","submitted_at":"2025-10-27T16:07:36Z","title":"Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T07:55:01.225653Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2510.23469"},"observation_digest":"sha256:64501cc852167a06cd02dafceb5dc37d75487b1e722e0f6791a1aab846c7d28f","observation_id":"d8a73c8f-2169-49cc-acde-66c0e45162e4","resolution":{"observed_at":"2026-08-04T07:55:01.225653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2511.06216","last_updated":"2026-05-10T15:35:17Z","snapshot_observed_at":"2026-08-08T15:51:50.031607Z","submitted_at":"2025-11-09T04:01:46Z","title":"Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-17T23:39:37.510194Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2511.06216"},"observation_digest":"sha256:3196bc6b55f735807491361d0c39d6165810cb0aef7fa886c9714f6a97716748","observation_id":"1d4e3404-f295-4af0-b5e2-cb2f82756cd5","resolution":{"observed_at":"2026-05-17T23:40:31.163731Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2511.07969","last_updated":"2026-04-07T13:05:33Z","snapshot_observed_at":"2026-08-06T09:52:07.214838Z","submitted_at":"2025-11-11T08:28:26Z","title":"Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-18T00:04:47.376473Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2511.07969"},"observation_digest":"sha256:d47385e14d3a955ea48d715381c49a2cd10b3f732c7b058fa503b9ee1914f105","observation_id":"ae5238df-5cfb-4154-9e72-e2845f4c049e","resolution":{"observed_at":"2026-05-18T00:05:31.462917Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2512.00716","last_updated":"2026-04-21T05:43:35Z","snapshot_observed_at":"2026-07-06T22:37:21.536027Z","submitted_at":"2025-11-30T03:58:55Z","title":"Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-17T03:39:47.784096Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2512.00716"},"observation_digest":"sha256:f2a1c44828f9b7d164ae5f01ee52137efadfd4289240747cbc4fba5811368ccf","observation_id":"7404baf7-dd61-4424-bb2d-69daf4c53b24","resolution":{"observed_at":"2026-05-17T03:41:29.287785Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2512.24062","last_updated":"2026-04-07T08:07:30Z","snapshot_observed_at":"2026-07-06T22:40:23.205898Z","submitted_at":"2025-12-30T08:11:37Z","title":"Energy-Balanced Hyperspherical Graph Representation Learning via Structural Binding and Entropic Dispersion","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T19:20:32.868784Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2512.24062"},"observation_digest":"sha256:3e3687807e19abe4039482b53855b9d59d0216174acc8ed0b4f96bb48fbc3a50","observation_id":"78a99950-404d-460f-b44a-5ebcd88c2c69","resolution":{"observed_at":"2026-05-16T19:21:12.144293Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2602.11629","last_updated":"2026-05-22T02:58:25Z","snapshot_observed_at":"2026-08-02T17:31:07.804176Z","submitted_at":"2026-02-12T06:25:21Z","title":"GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T07:34:42.234291Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2602.11629"},"observation_digest":"sha256:39c8fd48c94c5590969127a4c6f0bf407bf62fc342560412dbf05df516eb2678","observation_id":"5c429c70-1012-4af1-8e32-e053dfa4c7dd","resolution":{"observed_at":"2026-05-25T07:35:28.041253Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-02T23:38:57.556834Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2602.13075","last_updated":"2026-05-27T04:21:31Z","snapshot_observed_at":"2026-08-02T23:38:48.433563Z","submitted_at":"2026-02-13T16:34:55Z","title":"Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-02T23:38:57.556834Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2602.13075"},"observation_digest":"sha256:d51ca12ce3d86ca1f9737a59f6c8664858674e49a9476574f7dcc057f89e4374","observation_id":"91b252fd-fe17-4b00-8de2-c21330a296c1","resolution":{"observed_at":"2026-08-02T23:38:57.556834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2604.14746","last_updated":"2026-04-16T07:57:11Z","snapshot_observed_at":"2026-07-29T20:39:55.580501Z","submitted_at":"2026-04-16T07:57:11Z","title":"Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T11:03:28.089449Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2604.14746"},"observation_digest":"sha256:bdcd3dab8a7c068a32363da34fe2476cb6c7442f2112e8d45e9a9e63b543b313","observation_id":"15ea55c1-3f1e-4901-b2e3-95dfa1d17c5c","resolution":{"observed_at":"2026-05-10T11:05:08.474715Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2604.19028","last_updated":"2026-04-21T03:23:34Z","snapshot_observed_at":"2026-07-06T23:05:44.499005Z","submitted_at":"2026-04-21T03:23:34Z","title":"Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors","version":1},"reference_index":158,"source":"arxiv_source","source_observed_at":"2026-05-10T03:50:44.626261Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2604.19028"},"observation_digest":"sha256:f138ce8836d6235064b9020c9b774967d580914c76e2e960c30b99ee1ca90ef7","observation_id":"84013cf5-101a-4a28-a2ac-b49f5066eca2","resolution":{"observed_at":"2026-05-11T12:21:04.901778Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.05463","last_updated":"2026-05-06T21:38:38Z","snapshot_observed_at":"2026-07-06T23:18:02.987518Z","submitted_at":"2026-05-06T21:38:38Z","title":"Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-08T17:14:19.708464Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.05463"},"observation_digest":"sha256:9bf2898f9e3451163a672ebd6d220c2cbc74a144d56ea042ad38c2a84cc65d3f","observation_id":"94605e06-5dcc-40a4-8bf1-1a042e6fd3e9","resolution":{"observed_at":"2026-05-11T17:46:08.132889Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.08178","last_updated":"2026-05-05T08:37:29Z","snapshot_observed_at":"2026-08-07T11:34:05.119101Z","submitted_at":"2026-05-05T08:37:29Z","title":"Generalized Category Discovery in Federated Graph Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-12T01:40:53.514118Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.08178"},"observation_digest":"sha256:7ec9c5f84b3c1f6540a790148d9d9587c47448e00ca20996e527d6fac983def9","observation_id":"fb8fae32-bd1d-4279-a93e-67c7c163fe8b","resolution":{"observed_at":"2026-05-12T01:41:19.675960Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.15888","last_updated":"2026-06-05T02:17:47Z","snapshot_observed_at":"2026-08-02T15:57:41.366470Z","submitted_at":"2026-05-15T12:19:18Z","title":"CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-20T20:16:26.671005Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.15888"},"observation_digest":"sha256:ae7e13915a5d816a9e00c2af210c70e50d4839efe982a9b27db9e4a8ddededd8","observation_id":"563a489b-c307-46ef-9928-1677e7217614","resolution":{"observed_at":"2026-05-20T20:18:59.782585Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.15888","last_updated":"2026-06-05T02:17:47Z","snapshot_observed_at":"2026-08-02T15:57:41.366470Z","submitted_at":"2026-05-15T12:19:18Z","title":"CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T19:16:15.616715Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.15888"},"observation_digest":"sha256:88a869a1c18da1ef24ffce0a03ce2bbf7f6d47cbd66c7f3ca349ffea32f243da","observation_id":"f91d6c4d-691b-4875-9a36-4a35e845a266","resolution":{"observed_at":"2026-07-01T14:55:47.464101Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.18579","last_updated":"2026-05-20T03:15:37Z","snapshot_observed_at":"2026-08-02T23:11:13.880944Z","submitted_at":"2026-05-18T15:56:19Z","title":"S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-20T12:49:18.671971Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.18579"},"observation_digest":"sha256:06b65ac334601058c31c51172afd4d1495b9eb773dc3eca6c2848947f21505da","observation_id":"37fa23e6-04c8-4203-ba26-84f44fddbf03","resolution":{"observed_at":"2026-05-20T12:53:17.667493Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.18579","last_updated":"2026-05-20T03:15:37Z","snapshot_observed_at":"2026-08-02T23:11:13.880944Z","submitted_at":"2026-05-18T15:56:19Z","title":"S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-21T07:55:11.088587Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.18579"},"observation_digest":"sha256:b69959019e0df44be702884ee3a5dbadeb48fbfa3f923d6109de050fd2c182bf","observation_id":"5c6fff83-7ce7-4425-8eb4-e332c84c74da","resolution":{"observed_at":"2026-05-21T07:59:50.847596Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.19916","last_updated":"2026-05-19T14:40:51Z","snapshot_observed_at":"2026-08-01T14:04:27.787769Z","submitted_at":"2026-05-19T14:40:51Z","title":"Fast and Featureless Node Representation Learning with Partial Pairwise Supervision","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-20T06:39:02.277629Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.19916"},"observation_digest":"sha256:645d2dc9cf1a26c555ac67ab64d6a78443ab9675ce7742263f4fc9f7d1416a66","observation_id":"1a6cd011-8e69-4543-900f-ad0489e0e999","resolution":{"observed_at":"2026-05-20T06:43:06.059710Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.26040","last_updated":"2026-05-25T17:06:13Z","snapshot_observed_at":"2026-08-05T07:16:17.547116Z","submitted_at":"2026-05-25T17:06:13Z","title":"L2IR: Revealing Latent Intent in Graph Fraud Detection","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-29T21:43:17.919274Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.26040"},"observation_digest":"sha256:f3a7ae6d642bfe4c619244e540296179618776632c39c24c7c7e109a42f73857","observation_id":"85759132-944b-4896-8245-f8feee5aa0f5","resolution":{"observed_at":"2026-06-29T21:43:58.699797Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2605.28990","last_updated":"2026-05-27T18:48:31Z","snapshot_observed_at":"2026-08-03T08:48:49.512147Z","submitted_at":"2026-05-27T18:48:31Z","title":"Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T13:31:36.276306Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2605.28990"},"observation_digest":"sha256:ecbe9503a67f6baf69df6b382b507f772ef7e98329836b246b04b5baa488d21a","observation_id":"5235aadb-85ff-4764-ac2e-52b8f936fa15","resolution":{"observed_at":"2026-06-29T13:33:27.905514Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2606.03270","last_updated":"2026-06-02T07:35:42Z","snapshot_observed_at":"2026-08-03T02:15:00.733413Z","submitted_at":"2026-06-02T07:35:42Z","title":"Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-06-28T11:44:32.359058Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2606.03270"},"observation_digest":"sha256:22facdde0666a1482f8e292f76dd9e9f421a72036fd0fe3ea48b1c1c0f615956","observation_id":"498fcabd-17f8-495a-b0d0-08cb4cfb1486","resolution":{"observed_at":"2026-07-02T01:36:25.651275Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2606.03307","last_updated":"2026-06-03T03:07:00Z","snapshot_observed_at":"2026-08-07T14:34:07.062842Z","submitted_at":"2026-06-02T08:21:57Z","title":"Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-28T08:25:09.062736Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2606.03307"},"observation_digest":"sha256:aef707e115438e4ce2a1f81c857c554b1dcd42e03c140c3673852787489faea8","observation_id":"24bd71e2-1469-4853-b1a2-9efc4d7094f1","resolution":{"observed_at":"2026-07-02T05:16:39.239819Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2606.03315","last_updated":"2026-06-02T08:27:03Z","snapshot_observed_at":"2026-08-02T01:16:29.098858Z","submitted_at":"2026-06-02T08:27:03Z","title":"A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T11:34:22.107760Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2606.03315"},"observation_digest":"sha256:b496941a9f5c19993d9e6e5e07067d7ab912eeae729519080e8d0bbf8230af21","observation_id":"3ffd3573-075d-401d-adcd-64b4917fe482","resolution":{"observed_at":"2026-07-02T01:46:26.702542Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2606.10284","last_updated":"2026-06-09T01:13:44Z","snapshot_observed_at":"2026-08-06T23:41:36.397111Z","submitted_at":"2026-06-09T01:13:44Z","title":"Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T13:51:32.704225Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2606.10284"},"observation_digest":"sha256:3de2ce8cc39abe7d6c73611b2cfe363162da3c83b10fb793d88a75ea8bcf2e1a","observation_id":"ac2938d4-9e31-4934-a2c4-020fd7eff782","resolution":{"observed_at":"2026-07-03T04:27:37.380568Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2606.11898","last_updated":"2026-06-11T03:22:55Z","snapshot_observed_at":"2026-08-07T05:39:04.404942Z","submitted_at":"2026-06-10T10:25:59Z","title":"GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-27T09:41:14.904868Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2606.11898"},"observation_digest":"sha256:014e32b53ba9479e3f0385b15e93862ee95f8ef649ba5d0525ed8534af17de4f","observation_id":"5bc2f580-dbd4-4065-a3f8-256c0dd63c9f","resolution":{"observed_at":"2026-07-03T11:08:03.188665Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2606.17667","last_updated":"2026-06-16T08:29:34Z","snapshot_observed_at":"2026-08-03T03:23:46.290922Z","submitted_at":"2026-06-16T08:29:34Z","title":"Handling Feature Heterogeneity with Learnable Graph Patches","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-27T01:36:45.977332Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2606.17667"},"observation_digest":"sha256:7f2797fa82f89aa79a2febd606043d53ee3df18b36f761eda2f6d7b0573b7737","observation_id":"e255a7ed-1572-49fb-946d-7f34111d4100","resolution":{"observed_at":"2026-07-03T20:08:56.316003Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":"2006.04131","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-03T20:08:56.312991Z","title":"Deep graph contrastive representation learning.arXiv preprint arXiv:2006.04131","venue":null,"work_id":"cb5de7fe-f75b-451e-a2ef-3758a044c358","year":2020},"citing_paper":{"arxiv_id":"2607.00377","last_updated":"2026-07-01T03:21:23Z","snapshot_observed_at":"2026-08-02T19:24:02.421330Z","submitted_at":"2026-07-01T03:21:23Z","title":"SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-07-02T16:42:20.573637Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2607.00377"},"observation_digest":"sha256:03d65490e955dd0d7819f0b0c182eacbcf586d12c523432d07eb1a59787a3ab1","observation_id":"10b1f488-7e61-473f-86cc-841a25b218c5","resolution":{"observed_at":"2026-07-02T16:47:09.020016Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-07-12T05:36:37.592335Z","title":"Renguang Zuo, Yihui Xiong, Ziye Wang, Jian Wang, and Oliver P","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.02990","last_updated":"2026-07-03T05:56:24Z","snapshot_observed_at":"2026-08-09T16:16:02.113385Z","submitted_at":"2026-07-03T05:56:24Z","title":"MABLE: Masked Autoencoding with Bi-Lipschitz Decoding for Embeddings and Graph Metric Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-12T05:36:37.592335Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2607.02990"},"observation_digest":"sha256:79f3f4e139e2f3744b13d7c5f9f64d7ed575d676caa4ddb93e516fcd8f212dbc","observation_id":"a4220813-3c0d-439e-8d8b-204232fc04cd","resolution":{"observed_at":"2026-07-12T05:36:37.592335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-01T22:39:15.789523Z","title":"Deep graph contrastive representation learning,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.15687","last_updated":"2026-07-17T06:57:41Z","snapshot_observed_at":"2026-08-08T19:14:01.986065Z","submitted_at":"2026-07-17T06:57:41Z","title":"Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T22:39:15.789523Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2607.15687"},"observation_digest":"sha256:4c263018642f131c65e232924dd457e27dc217a7fd242cd547ff0513ff9ba565","observation_id":"ef2bdb3a-11dd-451b-9fda-d01c9ecfb5c3","resolution":{"observed_at":"2026-08-01T22:39:15.789523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-01T18:21:16.892921Z","title":"Deep graph contrastive representation learning,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.17342","last_updated":"2026-07-19T17:05:46Z","snapshot_observed_at":"2026-08-09T02:23:25.183795Z","submitted_at":"2026-07-19T17:05:46Z","title":"STAR: Skeletal Token Alignment and Rearrangement for Interaction Recognition","version":1},"reference_index":110,"source":"pdf_text","source_observed_at":"2026-08-01T18:21:16.892921Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2607.17342"},"observation_digest":"sha256:11438e8c03947b39d5c6984d71fec56aa135e29aa8a72592353cdeaee31dab87","observation_id":"3c46f1cd-cdea-4115-9857-5cfe02558498","resolution":{"observed_at":"2026-08-01T18:21:16.892921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04131","snapshot_observed_at":"2026-08-08T18:41:07.460745Z","title":"2006.04131 , archivePrefix=","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2608.04381","last_updated":"2026-08-05T02:39:28Z","snapshot_observed_at":"2026-08-09T07:13:52.160972Z","submitted_at":"2026-08-05T02:39:28Z","title":"NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T18:41:07.460745Z"},"links":{"cited_paper":"/paper/2006.04131","citing_paper":"/paper/2608.04381"},"observation_digest":"sha256:40b470d4fbd773a1ac7451c9ab8de60d57bf7021f8babf5b44dd90952268a622","observation_id":"c83e475c-e1c2-482d-a0f0-90cb5565b64f","resolution":{"observed_at":"2026-08-08T18:41:07.460745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2006.04131/citation-record","integrity":"/paper/2006.04131/integrity","json":"/paper/2006.04131/citation-record.json","paper":"/paper/2006.04131"},"outbound":[],"paper":{"arxiv_id":"2006.04131","last_updated":"2020-07-13T16:32:20Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T07:13:23.894027Z","submitted_at":"2020-06-07T11:50:45Z","title":"Deep Graph Contrastive Representation Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 51 inbound Pith citation observations for arXiv:2006.04131."}