{"as_of":"2026-08-11T19:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ce2f9fd759173c46902a081a8c76b4f6be08391c08c322de19e7fcd2e7735000","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:45:03.507093Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2501.04102/citation-record","integrity":"/paper/2501.04102/integrity","json":"/paper/2501.04102/citation-record.json","paper":"/paper/2501.04102"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.122000Z","title":"Arnetminer: extraction and mining of academic social networks,","venue":null,"work_id":"9d4eb084-43f2-4a41-8747-5a462f7ce550","year":2008},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.286790Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:04ed63f65b9d42c0bad85d61189ae17b12afdf7c5e3589011c69c2c6481b4c41","observation_id":"12468a6a-869c-4260-af65-c4b5251ecb83","resolution":{"observed_at":"2026-08-10T21:45:04.125630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.111892Z","title":"Inferring networks of substi- tutable and complementary products,","venue":null,"work_id":"89cee04a-774c-4e22-8b6d-595a278f21fa","year":2015},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.290888Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:193588472c6f440767455ea2f0c203b7dd4710a984b8dfea5bd7fbff04478ecf","observation_id":"44087d52-179b-47a6-a7b4-a6ac6b999776","resolution":{"observed_at":"2026-08-10T21:45:04.115617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.102397Z","title":"Meta- gnn: On few-shot node classification in graph meta-learning,","venue":null,"work_id":"985898f9-35b8-4e0a-93a5-ec823e3276e7","year":null},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.294636Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:4cd86069b6280bdbed6080fae119625dca865eceb45bef355eb53ec2cab55090","observation_id":"cf7e3457-f7be-44d4-90e9-5a53709380da","resolution":{"observed_at":"2026-08-10T21:45:04.106159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.093135Z","title":"Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking,","venue":null,"work_id":"45014040-d132-414d-a0d8-5fa0d2fd9419","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.298759Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:3f1d181f38adb4e5f76770a4526d63e4fed8d7beedfab0731e01551556a29584","observation_id":"8399508f-962d-4ae0-9efc-db5d80b57881","resolution":{"observed_at":"2026-08-10T21:45:04.096648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.083258Z","title":"Graph few-shot class-incremental learning,","venue":null,"work_id":"37dc08d3-1381-44c2-aa1e-b38f988c0c40","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.302389Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:d857cd40a79ecad8797b37675695e24d1927385915006cb51be90d4f141cd09d","observation_id":"b81541d2-f64d-4462-b9a4-e48baeae5be1","resolution":{"observed_at":"2026-08-10T21:45:04.086931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.073681Z","title":"Contrastive meta-learning for few- shot node classification,","venue":null,"work_id":"9de54e36-bdcc-4f57-bb10-0d591c785ea7","year":2023},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.306347Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:f652ec6b508be6a38d3287563ddd9018ff8dfb76bd864e40c612053aca858c8d","observation_id":"54f6a890-fd67-4006-99bf-7fc0829964a6","resolution":{"observed_at":"2026-08-10T21:45:04.077213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.064135Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":"85b28be0-b9cc-4db2-b035-a78a440c63aa","year":2017},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.310378Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:fe946762bf0547e084f0fda524d414c12efa19a783ed76d3e124567dcd399b48","observation_id":"302c90f6-a4ba-4e95-b681-9946e26e8fa3","resolution":{"observed_at":"2026-08-10T21:45:04.067485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.054254Z","title":"Graph attention networks,","venue":null,"work_id":"0527e310-1ca8-43d5-b2e4-46e743228b8b","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.314402Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:4d51b12001f33800199f06b39c969b635100387cb3505a718c262b0402aa5246","observation_id":"c00499dd-e037-4627-b9e0-5908ab0d822b","resolution":{"observed_at":"2026-08-10T21:45:04.057574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.044734Z","title":"Graph neural networks: A review of methods and applications,","venue":null,"work_id":"68e09c7e-646a-42eb-a084-a23371aca6d5","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.318075Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:9552ab249fe3d5cc6c6b891a1201c8f665f3832b754497e7f954783a739162db","observation_id":"30e5ec93-5317-4a59-9387-a71ef0918e5a","resolution":{"observed_at":"2026-08-10T21:45:04.048213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.035113Z","title":"Heterogeneous network embedding via deep architectures,","venue":null,"work_id":"6921d35c-f2d4-4dd2-8217-87456d6e7557","year":2015},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.321953Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:d4e1d9f0959ad555d7947364daa475efc02d3c1b0ba7132d4cc6ac0ffaf10f8d","observation_id":"8947cdb3-1c26-4945-8e2d-72940371649b","resolution":{"observed_at":"2026-08-10T21:45:04.038909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.025018Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":"22a274f8-2a03-47b3-a5e2-593303f7df38","year":2017},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.325505Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:40be7ae81296e3cf8daaac3ab4e49ba0c2112517d3fd841238d3a325f6d4323e","observation_id":"d49a10f0-03b6-4507-98ea-4fbe7c88863b","resolution":{"observed_at":"2026-08-10T21:45:04.028572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.015051Z","title":"How powerful are graph neural networks?","venue":null,"work_id":"d20d514f-8325-4008-9871-a61a0e90091a","year":2019},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.329495Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:b3c8cb2ead6f65c01e43c92fcb3e5117977f070aaf686167ff9ca4bb5924a29b","observation_id":"81d3cc92-e2cb-41f5-82c3-9a8ea1d4c795","resolution":{"observed_at":"2026-08-10T21:45:04.018436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:04.005034Z","title":"Graph few-shot learning with task-specific structures,","venue":null,"work_id":"249f55bf-c411-47c0-a176-48bdc3030e7d","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.333151Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:ec4f672d277f4ca403e7fde909a297b1cb9f3ab4b7cf11867b211b2187e0a654","observation_id":"a100ac12-14e4-4cb1-beb8-b5d8d0ff24db","resolution":{"observed_at":"2026-08-10T21:45:04.008674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.995822Z","title":"Deep neural networks for learning graph representations,","venue":null,"work_id":"e1abe9af-389c-4e81-a006-e1ab883c754a","year":2016},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.336992Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:db5c371cd6c5307201f549c95bc8c904379986ccc608863da9369d9fdcddbaa3","observation_id":"f2b9ea0d-4a6c-41f8-af96-bd1a2d9213f6","resolution":{"observed_at":"2026-08-10T21:45:03.999287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.986251Z","title":"Interpretable and generalizable graph learning via stochastic attention mechanism,","venue":null,"work_id":"b60a64dd-96f5-4455-b63a-563d0dcfa9e2","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.340911Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:449ec9e303ba0aa6cce73962ff8ff5558a5b61ea0e8d1eaed9916b5d2159e1d8","observation_id":"f3bbceee-889a-46c5-85d9-6715ca58a25d","resolution":{"observed_at":"2026-08-10T21:45:03.989876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.976680Z","title":"Mind the label shift of augmentation-based graph ood generalization,","venue":null,"work_id":"180e7897-c3cb-4932-9c2e-c77ef29092fa","year":2023},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.344633Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:4539a757298b897d05508a943cba5db9c569ce527311bc14a80112b1a68e84c0","observation_id":"9c3331fa-6900-4761-8bdc-7c55ca059090","resolution":{"observed_at":"2026-08-10T21:45:03.980199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11034","last_updated":"2024-05-17T18:11:11Z","snapshot_observed_at":"2026-08-05T23:40:56.641675Z","submitted_at":"2024-05-17T18:11:11Z","title":"Safety in Graph Machine Learning: Threats and Safeguards","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11034","snapshot_observed_at":"2026-08-10T21:45:03.348293Z","title":"Safety in graph machine learning: Threats and safeguards,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.348293Z"},"links":{"cited_paper":"/paper/2405.11034","citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:b9c8d83aa1546e3f29704565e345279fcc8c9fa3209e8413122d9cd73440692e","observation_id":"7f21e6ec-39fe-4c96-9c75-6080b8bedfc4","resolution":{"observed_at":"2026-08-10T21:45:03.348293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.966872Z","title":"Handling distribution shifts on graphs: An invariance perspective,","venue":null,"work_id":"c781022a-ddf1-4a4e-be54-77868b066c1c","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.352373Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:6b394ca3a37de1de1ad97a068d905adb8a79dbd1d76a59ace35b354909054745","observation_id":"b90897a1-f3c5-4d97-9a29-de33dacf46aa","resolution":{"observed_at":"2026-08-10T21:45:03.970600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.956636Z","title":"Graphrnn: Generating realistic graphs with deep auto-regressive models,","venue":null,"work_id":"54d0090b-f5c4-4a73-a0e0-be6c6737e41d","year":null},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.356092Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:aca0ecb1a592ee5e85885c6cbfa00e3c8311f3bd2944e278924df650e228a3c4","observation_id":"bd57bd47-a4f3-4b31-b3c9-bd6a46f0b5b8","resolution":{"observed_at":"2026-08-10T21:45:03.960669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.946098Z","title":"Graph neural networks with convolutional arma filters,","venue":null,"work_id":"5a24f0e2-cc74-4860-b0d9-0e409bd072ac","year":2021},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.359699Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:94c6b8a369781cf1b969adfbc1d267ea3d730758ad2d8048aed0d638d5ea32a9","observation_id":"065f81f9-dd1b-42e8-8b44-3ce18fa9c9d1","resolution":{"observed_at":"2026-08-10T21:45:03.949720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.935690Z","title":"Domain adaptation: Learning bounds and algorithms,","venue":null,"work_id":"c90b624e-a67a-4fff-a385-1cfce05fce53","year":2009},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.363596Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:62aa93d9288b2db657731809d3999e9bc58c894d3a1a7f2a8413127403bc9dd2","observation_id":"4f189ba8-bc1c-450f-8098-d09045027e88","resolution":{"observed_at":"2026-08-10T21:45:03.939422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.924281Z","title":"Generalizing from several related classification tasks to a new unlabeled sample,","venue":null,"work_id":"c1377f4c-bc2f-4ea5-a1a9-c8df8a26d001","year":2011},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.367268Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:cdc6c6f88b6a4fb8567efc21058d718200456c415c4b5e97e836ae2be0c40c03","observation_id":"314e145e-1290-4aa5-870a-63df16715185","resolution":{"observed_at":"2026-08-10T21:45:03.929016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.912098Z","title":"Domain generalization via invariant feature representation,","venue":null,"work_id":"f643318c-ed68-4a9a-b047-8eaad3ffb804","year":2013},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.370998Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:4adfa7ef3aaa6df36da9aceb8bbb2957e1e5c947b2ed770499ac702adf0e5c70","observation_id":"ecd51593-26ea-49b2-9df0-d4ffadf07bce","resolution":{"observed_at":"2026-08-10T21:45:03.915446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.903039Z","title":"Recognition in terra incognita,","venue":null,"work_id":"98bdfe4b-4a7c-45fb-9ba8-02513c65198a","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.374481Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:5f4efc4321a6f4ad2565433f10c284ab933333f885cfaee1431e40102b98b33e","observation_id":"0297c8b8-a392-4d61-aece-f2349c5218c0","resolution":{"observed_at":"2026-08-10T21:45:03.906199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.894233Z","title":"Do imagenet classifiers generalize to imagenet?","venue":null,"work_id":"6f3db868-164f-443f-9a26-4a8a31c891fc","year":2019},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.378075Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:4b9f6688f3bbbc92e53f659141a1caf4d1a45da3639bed7737075624955337b8","observation_id":"75152b5f-e389-4c41-965c-4593cce60223","resolution":{"observed_at":"2026-08-10T21:45:03.897416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.884979Z","title":"One pixel attack for fooling deep neural networks,","venue":null,"work_id":"6b7d8322-f67a-4e90-8553-c0027eef1d8d","year":2019},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.381867Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:43d79c1e9271dd7b5c8dc1001e8a2bf99d0c4ebb1ec13c15817cf6645b1f944d","observation_id":"2b8fe012-6ade-4bdc-9e72-830324ee8958","resolution":{"observed_at":"2026-08-10T21:45:03.888478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.875504Z","title":"Collective spammer detection in evolving multi-relational social networks,","venue":null,"work_id":"546b0652-c468-4a13-86f3-f818daa16301","year":2015},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.385481Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:0c455dd9a305b14173bc07f3043fca77ba5d49be685dcf1d040bf1036f71b1af","observation_id":"a4b700c4-2f16-43a8-bfb9-2f2c2943c765","resolution":{"observed_at":"2026-08-10T21:45:03.879298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.867197Z","title":"Good: A graph out-of-distribution benchmark,","venue":null,"work_id":"c3c8a848-d390-4611-a755-e29284d8f9ee","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.389251Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:8f716eccf62a7f25663d68a080e5bd7fee02e535f28ba61cfcb35cea7273858b","observation_id":"3f987b30-b16b-4806-8a82-a44e362a7af4","resolution":{"observed_at":"2026-08-10T21:45:03.870304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.858191Z","title":"Few-shot node classification with extremely weak supervision,","venue":null,"work_id":"d22d8829-ca79-401c-afdf-e1ce2d33754d","year":2023},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.392899Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:878f89fa9b8c53b60e3b1c43f53031b1a2a461bdb744f48b1e1ed65f5c421698","observation_id":"45e2db41-ac8e-433b-bdc2-d5bf5e86199e","resolution":{"observed_at":"2026-08-10T21:45:03.861790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.849046Z","title":"Out-of-distribution generalization via risk extrapolation (rex),","venue":null,"work_id":"94db48e4-ee96-428d-a8c3-9aa12c079757","year":2021},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.396592Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:0d6783f3ba88926d28c4bd124789d04d28275c4d17a90eb0a54d6c40ace97286","observation_id":"c9f531b8-d383-4048-85b2-4df5fe9cd111","resolution":{"observed_at":"2026-08-10T21:45:03.852337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.840577Z","title":"Invariant rationalization,","venue":null,"work_id":"33e11100-fdf8-445a-abe4-adef5e56b93b","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.400223Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:15b90c49f9e09892c5ab9bde2e736da8ab15a6772341d5c434e33f34518a5e24","observation_id":"f25b0c4e-6bbd-4fef-90f3-2262f8e299e8","resolution":{"observed_at":"2026-08-10T21:45:03.843799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.830969Z","title":"Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing,","venue":null,"work_id":"4539071d-27bc-44e5-a28d-c47598807394","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.403888Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:23c9e19d170d3c0ad97450c1d43567b2e82bb44eb6e566f53b34d840540b5398","observation_id":"46380d06-1aa6-4d4d-bf1a-116c253bc09b","resolution":{"observed_at":"2026-08-10T21:45:03.834510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.820680Z","title":"Dark model adaptation: Semantic image segmentation from daytime to nighttime,","venue":null,"work_id":"94f02017-8c1a-45cc-be76-aaae6190ceae","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.407300Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:b0bf9fa4f50a11aafb108fd80868185bbdb7f48753bb9e0cc6a2393826de22e3","observation_id":"18260e13-ff5c-4da6-aa54-d87f5daec8ab","resolution":{"observed_at":"2026-08-10T21:45:03.824340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.810345Z","title":"Discovering invariant rationales for graph neural networks,","venue":null,"work_id":"af52cfcd-09dc-4469-9a95-d0ae7a7397d0","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.410919Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:efa0553f8d4fdb7f9f9e7985006f3a81f671e2c0fba128d86582f36ff00bf008","observation_id":"97373fd0-7bff-41ad-b1c9-3c0b3c1f25f6","resolution":{"observed_at":"2026-08-10T21:45:03.813879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.800855Z","title":"Invariant risk minimization,","venue":null,"work_id":"2d0110c7-337f-4a64-83a1-f8b353bdde39","year":2019},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.414417Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:f28d062b0ff9f5bfe0ad276a64ba7745024171618fe2ec966d65b1c13670b450","observation_id":"7b6b9c39-9c86-4091-8c0c-80092b9d783e","resolution":{"observed_at":"2026-08-10T21:45:03.804530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.791912Z","title":"Unsupervised domain adaptation by backpropagation,","venue":null,"work_id":"5af50782-14df-4f98-a616-2bf4bb35beaa","year":2015},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.418251Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:09a11dab402eca3496ddc252f0e6c92aaef7927213be7b804d333b5b33418c39","observation_id":"2c6a2150-4fd0-4a8e-aa6d-7dbf783cd020","resolution":{"observed_at":"2026-08-10T21:45:03.795281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.782746Z","title":"Domain generalization with adversarial feature learning,","venue":null,"work_id":"94842a8e-f178-4a98-92e1-077095ad7fb0","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.421961Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:7240db87f41dc6676361590668ff8b31dccb4f1426e459f6166b377c1f724384","observation_id":"90067a74-e124-4a40-bf57-a3808682a4b5","resolution":{"observed_at":"2026-08-10T21:45:03.786290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.771653Z","title":"Graph prototypical networks for few-shot learning on attributed networks,","venue":null,"work_id":"bd95b6e0-c10b-4d00-8fba-b725675cdc41","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.425300Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:96af8face658b3f33ffbcceebbb480043d2f0334646a1a87705455e10cee05d4","observation_id":"5d313ff3-6e75-472e-9ed1-cfbe10c29909","resolution":{"observed_at":"2026-08-10T21:45:03.776145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.762387Z","title":"Return of frustratingly easy domain adaptation,","venue":null,"work_id":"eec765f7-3ab8-433d-9bf2-b58c387bc16c","year":2016},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.428862Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:9798b3c885cc07d9d492ea9576ab85cbe9e24b21bd386eafe71966e85ef09e05","observation_id":"a89c6335-0088-404b-966f-20e897d1edff","resolution":{"observed_at":"2026-08-10T21:45:03.765864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.753366Z","title":"Invariance, causality and robustness,","venue":null,"work_id":"a25ce057-1ca9-4b17-ae22-0a481c846e63","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.432485Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:83fc12960ad1ef85513a3ab1c1b9ea1d7794c1389e6afb1f8cb377bf3c7e2930","observation_id":"f6ffcef4-5352-4d68-8d42-c4e0f2823f96","resolution":{"observed_at":"2026-08-10T21:45:03.756519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.744197Z","title":"Invariant causal prediction for nonlinear models,","venue":null,"work_id":"e39712a9-2a67-423f-8906-2f9d40da44f9","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.436152Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:928c9a19780daeb2a8c9c50aebc0b070d1a5514e0ed972bbcddd86ed10723a29","observation_id":"4785ccdd-33c9-4092-95d5-3da1712a942a","resolution":{"observed_at":"2026-08-10T21:45:03.747483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.734628Z","title":"Does distributionally robust supervised learning give robust classifiers?","venue":null,"work_id":"6140d42b-a7e2-4f46-94ca-8d2458670d42","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.439739Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:f0d00db670ce1db313d67f437824e96ec99d1594ec21b30551ca6516b8863d13","observation_id":"13f16d0c-0ee8-4cdf-a086-1d6e5a1e3e05","resolution":{"observed_at":"2026-08-10T21:45:03.738432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.724615Z","title":"Robust optimization over multiple domains,","venue":null,"work_id":"97fdf9e3-26d2-47c1-aa6b-0b86608a010f","year":2019},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.443163Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:ed146f663a165747095d55a969f7ce98d1d9f10f78b231e5f7b2235a29901c04","observation_id":"73f7cb80-1e57-41da-9dff-ecb33b170df1","resolution":{"observed_at":"2026-08-10T21:45:03.728520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.715137Z","title":"Distributionally robust neural networks,","venue":null,"work_id":"4048237b-2325-412e-8298-f721f79adb80","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.446943Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:e534d0250cb6cde90a3e78035deffe12af260a5d2cb9e2ebb7dc9f53f0764cbf","observation_id":"0533091b-11d1-4d5f-862a-4f95295c216f","resolution":{"observed_at":"2026-08-10T21:45:03.718759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.705669Z","title":"Adversarial weight perturbation improves generalization in graph neural networks,","venue":null,"work_id":"a106814b-8ff7-4967-bbb5-a75d2b8500bf","year":2023},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.450471Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:4666eda7366773e26be9503e94295d2e88c1e3be77de30e96a3c4e54fbf5e22c","observation_id":"589d58e7-a91c-4e07-9ecd-d16244519242","resolution":{"observed_at":"2026-08-10T21:45:03.709157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.02843","last_updated":"2023-12-21T06:43:36Z","snapshot_observed_at":"2026-08-10T03:02:04.901680Z","submitted_at":"2022-11-05T07:55:55Z","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","version":2},"cited_work":{"arxiv_id":"2211.02843","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.02843","snapshot_observed_at":"2026-08-10T21:45:03.569524Z","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","venue":"cs.LG","work_id":"66f0a3e4-5489-4b54-a3e2-bd408644e37c","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.453992Z"},"links":{"cited_paper":"/paper/2211.02843","citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:3fa496e10101c89ce2e023d46365897dbbe976becf6dcdae9a99712053d7fd56","observation_id":"653b3694-ffdf-4d65-af43-988a023c3dfa","resolution":{"observed_at":"2026-08-10T21:45:03.575804Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.696255Z","title":"Learning invariant graph representations for out-of-distribution generalization,","venue":null,"work_id":"b4823bba-8564-421a-b2e8-058245a5f6a4","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.457757Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:7022702e228a3448453d54b01ad9efa9f933d04390a61c279446782ae8b7fa2e","observation_id":"36ec7b7c-8932-4fd8-9cf8-999045cbf60f","resolution":{"observed_at":"2026-08-10T21:45:03.699773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.686919Z","title":"Learning causally invariant representations for out-of- distribution generalization on graphs,","venue":null,"work_id":"1c6cc292-1429-4634-9a8b-903a09b80d6e","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.461186Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:0861001cb8dd5afa9024580077acfcad5939d7bb5a4735b3c1d2e5e29af13fa0","observation_id":"c672bfbf-161e-4356-bb7f-07cef1810158","resolution":{"observed_at":"2026-08-10T21:45:03.690351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.677094Z","title":"Debiasing graph neural networks via learning disentangled causal substructure,","venue":null,"work_id":"43eb77a3-89df-4f75-8aac-ab779cb51086","year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.464902Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:673dfe151f1d045d9bb131be449f357b6d8eea74b48412a902686c0fab1f0027","observation_id":"8c60d2af-6141-4453-b52b-23c656961990","resolution":{"observed_at":"2026-08-10T21:45:03.680615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.666848Z","title":"Does in- variant graph learning via environment augmentation learn invariance?","venue":null,"work_id":"73082d2c-f761-47d1-be17-590bcd07637e","year":2023},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.468539Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:0b26d3ce542a7505364ab947e56552c5d5ff620cdb4dcd6e131f44441de24c70","observation_id":"4410f838-7e8f-4ec1-9ab0-d9b65d469261","resolution":{"observed_at":"2026-08-10T21:45:03.670619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.656367Z","title":"Categorical reparameterization with gumbel-softmax,","venue":null,"work_id":"9429b5a2-5ed9-4eb2-9359-382818d21c84","year":2017},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.472350Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:27767cd61474a8138c96210270920a0af6ed6abaae1abbd1013f76188414643c","observation_id":"524b4a31-6da5-4b62-a459-2bc25700ea6e","resolution":{"observed_at":"2026-08-10T21:45:03.660209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.644905Z","title":"Causal attention for interpretable and generalizable graph classification,","venue":null,"work_id":"8e31f146-2224-4438-8225-1db941f1e7c7","year":null},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.476349Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:c2e21b147deccd21b11a84f0a698b24d29afdd9ab4b115b6f6f280b026f63936","observation_id":"8e974dd4-f788-47d8-be76-88f4d1a634d1","resolution":{"observed_at":"2026-08-10T21:45:03.649072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-10T21:45:03.480383Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.480383Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:5dab2d9eb8d0c1e07a47d7cc63eaaeb9a45a936ed655f2850b36bfa62dcd6869","observation_id":"b4b8b0cc-391b-4a18-bec6-45964444fed5","resolution":{"observed_at":"2026-08-10T21:45:03.480383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.633478Z","title":"Parameterized explainer for graph neural network,","venue":null,"work_id":"fb395781-b091-4be6-9fa9-beb3d0b7d6af","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.484655Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:f56a2594c1babbf5643a73e3d591dfb94335d6696a8e3b19742addcad1c4ace6","observation_id":"a675727f-2b30-4db6-84b8-d942a37157f4","resolution":{"observed_at":"2026-08-10T21:45:03.637268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.09637","last_updated":"2022-01-24T12:32:48Z","snapshot_observed_at":"2026-07-06T12:30:38.345126Z","submitted_at":"2022-01-24T12:32:48Z","title":"DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.09637","snapshot_observed_at":"2026-08-10T21:45:03.488350Z","title":"Drugood: Out-of-distribution (ood) dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.488350Z"},"links":{"cited_paper":"/paper/2201.09637","citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:d52f691f908fe341d1eecbbdb5d1b1ef1a7622232a81d84d53c17a6f7017c1a8","observation_id":"c2218606-fefd-45d2-9c09-250befc49267","resolution":{"observed_at":"2026-08-10T21:45:03.488350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.622580Z","title":"Gnnex- plainer: Generating explanations for graph neural networks,","venue":null,"work_id":"5bada375-39e7-4d06-8b7c-97d6fd176059","year":null},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.492463Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:5b340edd0a79a21e13b808dffe0797137e9735e969b5d311b6150ef01d15a137","observation_id":"30a842a5-f3d5-4185-b5bb-233242007b05","resolution":{"observed_at":"2026-08-10T21:45:03.626378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.611867Z","title":"Understanding attention and generalization in graph neural networks,","venue":null,"work_id":"b2e54aa3-e762-4ef2-82af-340e2a65d66f","year":2019},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.496216Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:bd21c05cb0b25aff8098f3f00644839ae359633678884d9b28736d446a0d7af7","observation_id":"62f10fad-aab8-4c56-957e-1f5ee2fc2777","resolution":{"observed_at":"2026-08-10T21:45:03.615274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.602856Z","title":"Moleculenet: a benchmark for molecular machine learning,","venue":null,"work_id":"fd988300-9053-4ebf-9636-876c36644153","year":2018},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.499740Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:3db41134b0854bd6699f7cb83986d8979bb81802d58af084c298b8a83b2091b0","observation_id":"e8e3f555-a73d-4955-af72-539fd5520cc4","resolution":{"observed_at":"2026-08-10T21:45:03.605871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:45:03.592650Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":"78df8763-fef0-423c-bb2b-043ffa203fec","year":2020},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.503117Z"},"links":{"citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:defb05471986d42021caeb246eb0159fd331eaff298e0a34488faf2b0aa85872","observation_id":"0e24241f-3f05-42ee-8949-e1fb5debbadd","resolution":{"observed_at":"2026-08-10T21:45:03.596387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.09430","last_updated":"2021-10-20T22:40:40Z","snapshot_observed_at":"2026-08-06T03:47:37.849087Z","submitted_at":"2021-03-17T04:08:03Z","title":"OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.09430","snapshot_observed_at":"2026-08-10T21:45:03.507093Z","title":"Ogb-lsc: A large-scale challenge for machine learning on graphs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T21:45:03.507093Z"},"links":{"cited_paper":"/paper/2103.09430","citing_paper":"/paper/2501.04102"},"observation_digest":"sha256:0cdd06d3f89c76344908e5e745a9eae7ee0b066d4942e087450d40b07aace98f","observation_id":"1a12bb70-ec9b-4d5e-b143-2cbb301fb9c0","resolution":{"observed_at":"2026-08-10T21:45:03.507093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.04102","last_updated":"2025-01-07T19:19:22Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T17:51:03.526874Z","submitted_at":"2025-01-07T19:19:22Z","title":"Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":55},"total_outbound_references":60},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2501.04102."}