{"as_of":"2026-08-09T09:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b313771e5933ecce9770fc4220526118f2e12f60a786788f44e1037636fe4e22","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-16T09:48:18.217276Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2601.22427/citation-record","integrity":"/paper/2601.22427/integrity","json":"/paper/2601.22427/citation-record.json","paper":"/paper/2601.22427"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Expo- nentially improving the complexity of simulating the weisfeiler-lehman test with graph neural networks","venue":null,"work_id":"324c6359-0c2c-49b2-acfc-4707fe743a1a","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:7358692c564af459ea7daa480640462ed4b52fbb875f7ac05cff747bada04b85","observation_id":"3a9600b1-b16e-4432-a4b8-ff55dbe27a4b","resolution":{"observed_at":"2026-05-16T09:50:50.603308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Evolution- ary dynamics of higher-order interactions in social networks.Nature Human Behaviour, 5(5):586–595","venue":null,"work_id":"7095a05f-61ea-40f0-93d5-a12f2baa36c9","year":2021},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:b3ea5dfb5696cb395bc8d38345b13cd495fbb35249041c2b7c773dfcfe304483","observation_id":"03ed14c0-2fb7-4571-a9d9-9a448e6f92da","resolution":{"observed_at":"2026-05-16T09:50:50.600429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Do we really need complicated model architectures for temporal networks? InInterna- tional Conference on Learning Representations","venue":null,"work_id":"7df15b9f-25cb-4392-bce2-1afe4c451f34","year":2023},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:39a9ebcbf83b6fbb03c0df408029b9fc9b2b46f1b59ecac03e0d6c4f4eb6a312","observation_id":"947358ac-d855-42ae-aa23-cc0e5a3f36ff","resolution":{"observed_at":"2026-05-16T09:50:50.581279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dy- namic fraud detection: Integrating reinforcement learn- ing into graph neural networks","venue":null,"work_id":"33866c54-26cb-46df-90fc-ddcb127f01c4","year":2024},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:2dd2330511c0ba22477302f87d626202660aea38df40e70bf33b57726e541d33","observation_id":"a19b8b01-cfbc-4b48-b8f2-4b42c2ac6eaa","resolution":{"observed_at":"2026-05-16T09:50:50.588456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Counterfactual expla- nations and how to find them: literature review and benchmarking.Data Mining and Knowledge Discov- ery, 38(5):2770–2824","venue":null,"work_id":"9f20dff6-e38b-40a7-8343-5bba765656c7","year":2024},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:eae6797b735e95e32dd1f0967c1aea587e53a96c0c7b86dec7499f454fbcbfe8","observation_id":"bc7697f7-a0fe-43cd-adc9-5878e12c64b0","resolution":{"observed_at":"2026-05-16T09:50:50.597622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Social recommendation via graph-level counterfactual aug- mentation.Proceedings of the AAAI Conference on Ar- tificial Intelligence, 39(1):334–342, Apr","venue":null,"work_id":"5b6db9ee-d018-4fbd-af7d-6ed0132659d6","year":2025},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:091882cfbdce1d7a9667d41926952f80345868f3b54b6e3ce2e0cf016a448b56","observation_id":"eb0c8727-ed0c-4c15-8930-34064c243540","resolution":{"observed_at":"2026-05-16T09:50:50.578332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs","venue":null,"work_id":"ecb35e59-c32e-47fd-aabe-68fce9e5e02d","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:3face48352ec239d81fc06089ac799652db7df10dc5ecbaab78a3fc3b795c87b","observation_id":"0f5fd9ff-e040-4526-956b-cdab6354cf87","resolution":{"observed_at":"2026-05-16T09:50:50.591959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Representation learning for dy- namic graphs: A survey.J","venue":null,"work_id":"1d9f306a-f30b-490f-b2e4-c5f8c006fb63","year":2020},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:506e7a2a3ba32dde96b37a713e67c53d8f507809c8c25111a28bcfeaba46fbb1","observation_id":"023a15cf-c202-4950-b9c4-ce20ae28d8be","resolution":{"observed_at":"2026-05-16T09:50:50.594675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On generating plausible counterfactual and semi-factual explanations for deep learning","venue":null,"work_id":"6aa86333-6029-4c72-8588-b8e6de1b26a1","year":2021},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:77c724239ded1559fca9cdf3c10ddf0eaa7f9e75bf7c0ab752283ee404eaa8f1","observation_id":"b0aaea05-bea8-4669-bb29-e82641c708b7","resolution":{"observed_at":"2026-05-16T09:50:50.584614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Predicting dynamic embedding trajectory in temporal interaction networks","venue":null,"work_id":"530b8adb-abeb-4b68-93fe-0191c17a1519","year":2019},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:eb66d2a83709c3809f42bf086aa38725625fc0d6953f6654befaa454674d70c8","observation_id":"435ccd7e-81ab-41d2-b381-9b063fce2d38","resolution":{"observed_at":"2026-05-16T09:50:50.566349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Neighborhood- aware scalable temporal network representation learn- ing","venue":null,"work_id":"477089b3-ad9a-4e86-be75-6824ceaf7776","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:1eb87054c3ea5f2b0ce3815c8cccf003cecdfffe3358ca22a86e64dc4adb3681","observation_id":"d0cfaed5-9fb9-425b-aed4-bf5b64bb4656","resolution":{"observed_at":"2026-05-16T09:50:50.560444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Streaming graph neural networks","venue":null,"work_id":"7dd0618b-c06e-47df-9bb5-1f729092eab9","year":2020},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:6932b92e2e1ce5171435a2ce1814760132a40411fad496fdce1ab7218b8eb3d3","observation_id":"e3baf220-ad64-4053-aae0-884cde0e4de3","resolution":{"observed_at":"2026-05-16T09:50:50.563621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Clear: Genera- tive counterfactual explanations on graphs.Advances in neural information processing systems, 35:25895– 25907","venue":null,"work_id":"7b30ada2-084b-40df-ac7e-c9a0e0bdf3b5","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:015681fb31602b1e6033d9fce71c10da34659cb1b30ecd7526b4c56915aab6d2","observation_id":"f0bd60dd-87f4-47b3-97d9-0580eeb2943c","resolution":{"observed_at":"2026-05-16T09:50:50.569190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Benchmarking counterfactual image genera- tion.Advances in Neural Information Processing Sys- tems, 37:133207–133230","venue":null,"work_id":"d455af9d-a50a-4cee-9545-87dfd35c1a64","year":2024},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:4260011c18a153ff3ff86e29b018793e6e6122e79a8994aaff8d61f653062f61","observation_id":"87374040-8fe7-472b-b58c-e51478a8c09a","resolution":{"observed_at":"2026-05-16T09:50:50.572439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards better evaluation for dynamic link prediction","venue":null,"work_id":"914c52bb-fcc3-4694-a0ce-c247dd6f3d01","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:c16e752b53b49b89dd715f15561baa5ffc75981cbb1ec6503598bee6432fe6e5","observation_id":"0ea6e17a-a850-4c41-b0ab-705f4eb03a1a","resolution":{"observed_at":"2026-05-16T09:50:50.575445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unifying evolution, explanation, and discernment: A generative approach for dynamic graph counterfactuals","venue":null,"work_id":"dd59ada2-439d-4d02-8ceb-7d23b4efe00c","year":2024},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:250f4bd7c8be0c772e14e9025be45e7af5af1a9ed7474abe71f557d813eea3cb","observation_id":"4ac6fb31-13d9-4200-b462-b4999359db45","resolution":{"observed_at":"2026-05-16T09:50:50.512257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cody: Counterfactual explainers for dynamic graphs","venue":null,"work_id":"6a16f5b1-c23e-4dab-8efe-ba73cb5721d5","year":2025},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:18074283e9d71650ca069a1215a943ac511973c53e72037fd379918181a44143","observation_id":"c41ff04e-0dae-470f-9141-5193fd960b65","resolution":{"observed_at":"2026-05-16T09:50:50.553933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Temporal graph networks for deep learning on dynamic graphs","venue":null,"work_id":"a7779f0d-1af5-4811-96d3-071886d6c7e1","year":2020},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:ee8e79589a7e816dcb226a9e0c73aa21fd5094aa8408378ebfcf5bf08e4e8e3e","observation_id":"16609a6e-9b94-4994-a73c-11a6d8caf1ea","resolution":{"observed_at":"2026-05-16T09:50:50.515232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Causal inference using po- tential outcomes: Design, modeling, decisions.Journal of the American statistical Association, 100(469):322– 331","venue":null,"work_id":"8b07bffa-faa6-42f9-9db8-9661626bee3d","year":2005},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:44f3148ad8801b787c35afb7fba96d7f07d5847c46888196d68de249e8582eb9","observation_id":"3740ec42-b3e0-45bc-b532-1e541de699b7","resolution":{"observed_at":"2026-05-16T09:50:50.547646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Counterfactual explana- tions can be manipulated.Advances in neural informa- tion processing systems, 34:62–75","venue":null,"work_id":"f263b523-5a62-45ef-b30c-1fe9f54cb247","year":2021},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:05f36a696bce0c8f2cf83c6686332151c0a745e901b6b1ffa2ad4c64ed15e9b0","observation_id":"8bb2092b-24dd-4c51-aa1b-a4fe4ca91ac6","resolution":{"observed_at":"2026-05-16T09:50:50.538406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning and evaluating graph neural network explanations based on counterfactual and factual rea- soning","venue":null,"work_id":"48c9215b-fe82-40af-92a7-d173c270f060","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:18cbb9d200a7464087d11fda901721f279326b424cc7ffd3ffeddf297bc174ff","observation_id":"a31e9617-e457-4c89-999a-cca9c775eeb6","resolution":{"observed_at":"2026-05-16T09:50:50.541571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Freedyg: Frequency enhanced continuous-time dy- namic graph model for link prediction","venue":null,"work_id":"14854401-bf00-49ca-978e-b95171f8cf56","year":2024},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:22875935dda50f7a748722698b632e875d4c3871e56be10feedb20076e4761cd","observation_id":"c4bf0700-87f7-4816-bc6c-6b6d8c04aacb","resolution":{"observed_at":"2026-05-16T09:50:50.544571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dyrep: Learn- ing representations over dynamic graphs","venue":null,"work_id":"03809e3e-7ec9-42cb-9cc8-a6afcd29fafe","year":2019},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:a901282757284799eaa7e70d5861b38a8c966ed468d566b356bb3a8e54bddc9b","observation_id":"bd76d9a0-dac5-4487-878a-69831b394fbd","resolution":{"observed_at":"2026-05-16T09:50:50.557211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2105.07944","last_updated":"2021-05-17T15:33:25Z","snapshot_observed_at":"2026-08-07T20:17:21.322116Z","submitted_at":"2021-05-17T15:33:25Z","title":"TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning","version":1},"cited_work":{"arxiv_id":"2105.07944","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2105.07944","snapshot_observed_at":"2026-07-03T20:48:56.378430Z","title":"arXiv preprint arXiv:2105.07944 , year=","venue":null,"work_id":"21d89797-1a98-463d-80e4-824ce8c68b7e","year":2002},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"cited_paper":"/paper/2105.07944","citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:04d21d26ee12d42a3868f235e8967f7a75cb9d9486a2676ef1baa5e9e79daaa2","observation_id":"4b802fe8-06e3-4dff-83f9-c678b1cc9ddc","resolution":{"observed_at":"2026-05-16T09:50:49.297004Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic graph transformer with correlated spatial-temporal positional encoding","venue":null,"work_id":"1323a706-0618-42a3-bbd4-8a3dd054d484","year":2025},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:320c1738c661908d498b78e406a04abd30ebea1fff9307fd785a44456f56fdd0","observation_id":"4169c079-3f0d-4345-b8be-dad28843aa41","resolution":{"observed_at":"2026-05-16T09:50:50.529526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Counter- factual data augmentation with denoising diffusion for graph anomaly detection.IEEE Transactions on Com- putational Social Systems, 11(6):7555–7567","venue":null,"work_id":"932328eb-5a8d-4493-b5b8-8c1b0e630515","year":2024},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:5dd189bd228004d1a5ea1f840314159e88e517821b022be11fa995d01819f5e8","observation_id":"af5bb2ad-7c75-4dd1-9e18-218c734c5c8f","resolution":{"observed_at":"2026-05-16T09:50:50.532711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fac- tual and informative review generation for explainable recommendation","venue":null,"work_id":"d153b177-6b15-4897-8768-beee2fc9fa08","year":2023},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:ad42475e5637423307a7c04a6013cf315fa5c4cbbf46dd8c70e867c4d4fbcbba","observation_id":"ed9a1830-04d4-4bc3-ad5e-2282cd671000","resolution":{"observed_at":"2026-05-16T09:50:50.520752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Inductive repre- sentation learning on temporal graphs","venue":null,"work_id":"16a78f91-1ef2-4ea0-87ec-6560e40d3508","year":2020},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:5690123f0758d4994a7a028437beaa12a5a7853e6a4f0da52bc82b36345e79b9","observation_id":"dfa354c5-5dee-4a01-aee9-31eb9268bb3a","resolution":{"observed_at":"2026-05-16T09:50:50.523628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards better dynamic graph learning: New archi- tecture and unified library.Advances in Neural Infor- mation Processing Systems, 36:67686–67700","venue":null,"work_id":"5946011d-e126-48db-a696-ee8e920c447a","year":2023},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:ad5fc87fadbead2992ff9b3bf067430ba4911cec6dc06abaca46df99f63282a7","observation_id":"8f7e2b13-6bd9-470d-866d-bf25dd9135ee","resolution":{"observed_at":"2026-05-16T09:50:50.526724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In- ductive matrix completion based on graph neural net- works","venue":null,"work_id":"904edf3f-30a6-4b94-8c6a-5e1956d9620c","year":2020},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:54752251d23ab855105e48327410e585d11efe1e928b3f1b7f051f119f54e194","observation_id":"1425e391-2f39-4100-80b7-8bc6b7ce337f","resolution":{"observed_at":"2026-05-16T09:50:50.509243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An attentional multi-scale co-evolving model for dynamic link prediction","venue":null,"work_id":"9ea97078-45c0-482b-ae49-d490c49d1980","year":2023},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:218329a779ef898fa89d7fa6c1117f1cf75312c4f058fa1fb89291942565abfb","observation_id":"31b50076-abd2-4435-88bd-0705ea4c13ad","resolution":{"observed_at":"2026-05-16T09:50:50.535384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning from coun- terfactual links for link prediction","venue":null,"work_id":"b86ee9cc-7524-49f8-a6d6-9050e5a0d284","year":2022},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:6a9c0b48c1ea29f91c2e5cb78311da82b5af80fc62972e33fc3c42d099554a81","observation_id":"e957dbcd-2e8d-4a3e-a639-dad5acb468d1","resolution":{"observed_at":"2026-05-16T09:50:50.550773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7029bffb-6805-476a-a7eb-bf91bf30c1bd","year":2021},"citing_paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-16T09:48:18.217276Z"},"links":{"citing_paper":"/paper/2601.22427"},"observation_digest":"sha256:d783de9e7b8323a6b095bf27f966086b0a9005c8c20436f4ec780e22f5433b9e","observation_id":"0cbd4978-ad48-4c2b-96d5-98b3b5092814","resolution":{"observed_at":"2026-05-16T09:50:50.517829Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}}],"paper":{"arxiv_id":"2601.22427","last_updated":"2026-05-08T21:03:33Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T22:43:35.226546Z","submitted_at":"2026-01-30T00:41:20Z","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":33},"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 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2601.22427."}