{"as_of":"2026-08-14T19:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4aba7792588d210e76b2ef6e69a99925ceb12367951c41bff8e4c5a062aabe4f","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T19:25:40.794877Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T03:27:15.874345Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T03:29:21.729638Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"cited_work":{"arxiv_id":"2502.10111","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.10111","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv:2502.10111 (2025)","venue":null,"work_id":"a54a92af-f8da-4e8b-9138-97c2e926d9a7","year":2025},"citing_paper":{"arxiv_id":"2604.19372","last_updated":"2026-04-21T11:54:29Z","snapshot_observed_at":"2026-08-02T22:50:01.588582Z","submitted_at":"2026-04-21T11:54:29Z","title":"TACENR: Task-Agnostic Contrastive Explanations for Node Representations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T03:27:15.874345Z"},"links":{"cited_paper":"/paper/2502.10111","citing_paper":"/paper/2604.19372"},"observation_digest":"sha256:26af41cf5fff2bfea25b960be99bb589841e7144a5d0685692c603ce95165d77","observation_id":"ff99dae3-5f14-4f04-a369-047e6e0533f6","resolution":{"observed_at":"2026-05-10T03:29:21.730856Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.10111/citation-record","integrity":"/paper/2502.10111/integrity","json":"/paper/2502.10111/citation-record.json","paper":"/paper/2502.10111"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:40.663164Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.663164Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:a2849ced2aa2e1c6f940c57f6fe209e282ed882b2aacd6fa4c6c5f771591a695","observation_id":"a0457852-907b-4c3a-ac4d-5629bd26d7df","resolution":{"observed_at":"2026-08-07T19:25:40.663164Z","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":"2026-08-07T19:25:41.352371Z","title":null,"venue":null,"work_id":"a83af052-6177-4950-848c-dd5ab251ec0b","year":2021},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.667350Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:a7bf9616d82cbb6453d2a40ea9bed45c23e6f53dc457480fd0f52ac0f9ecc6b9","observation_id":"c669933e-6f14-4457-adb8-4f92713125a6","resolution":{"observed_at":"2026-08-07T19:25:41.356654Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.340408Z","title":null,"venue":null,"work_id":"aae66adf-de6d-420f-823a-4f667008b169","year":2005},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.671848Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:d09dc14b3feca2965123030028b4bc79ff71f740fb4ff7b95a63614e4f9cf8f4","observation_id":"264659ee-a742-44d6-b7b7-9056d8daee74","resolution":{"observed_at":"2026-08-07T19:25:41.343965Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19321","last_updated":"2023-10-30T07:41:42Z","snapshot_observed_at":"2026-08-13T09:16:44.571636Z","submitted_at":"2023-10-30T07:41:42Z","title":"D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion","version":1},"cited_work":{"arxiv_id":"2310.19321","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.19321","snapshot_observed_at":"2026-08-07T19:25:41.055285Z","title":"D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion","venue":"cs.LG","work_id":"6deb99d6-63dc-439c-b3e5-7d68ab8f76e8","year":2023},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.676233Z"},"links":{"cited_paper":"/paper/2310.19321","citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:d79f7c7ef3d3a99436517eae6220ec32e1aacedce553264d136eb482ce1dc6b1","observation_id":"158241bb-dea6-42ca-bbc6-690fef5bf253","resolution":{"observed_at":"2026-08-07T19:25:41.060434Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.329429Z","title":null,"venue":null,"work_id":"b15c9279-d5ba-47bb-86d1-705134192e84","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.680419Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:9215e94015a03e9574fe9d90ba06cfaf52f9fc7f5b87dc0093271d9ff43f8e66","observation_id":"9d901356-4654-4e87-bc48-2a422271088e","resolution":{"observed_at":"2026-08-07T19:25:41.333028Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:40.684181Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.684181Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:c4f9b5d9f2b8d9868ba80633fedd8e155f24eb5fd63877a59192651e7ac3ad97","observation_id":"416e01c7-a811-440f-8c69-6d9f65075ec7","resolution":{"observed_at":"2026-08-07T19:25:40.684181Z","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":"2026-08-07T19:25:41.312196Z","title":null,"venue":null,"work_id":"d3d1bbf9-cfc5-40e3-aeea-9b518f232e76","year":2018},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.688150Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:b78d0ab1828b3dfa394301b9c338d050474a1213a9a899e7ca8b2d000f7a2c74","observation_id":"36108690-829b-43bb-bcc0-2abf376d7936","resolution":{"observed_at":"2026-08-07T19:25:41.315770Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.038211Z","title":null,"venue":null,"work_id":"e351f5e2-ee9a-4507-992f-1339c630bac2","year":2024},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.691661Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:f3b876f166b310fe5f6cb7a199509d00a1d95d2d2b2a9de53dbd1e5f1e4c48b3","observation_id":"7d16391f-1d52-40ac-82d7-6b076c42105b","resolution":{"observed_at":"2026-08-07T19:25:41.042647Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.301956Z","title":null,"venue":null,"work_id":"fd315878-d298-4ae6-a284-fd422b58b3b7","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.695187Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:e6942f3e9e9f2779b1e8db7ed1d92f24c8c5d4bec5c866590c4b25edba295cc9","observation_id":"976133ed-f77b-4ab1-a71d-a7d4a1265a8e","resolution":{"observed_at":"2026-08-07T19:25:41.305779Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.291742Z","title":null,"venue":null,"work_id":"552e978c-d02d-4220-8f0a-63124f596194","year":2019},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.699211Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:d5dfef9dc36321dbfdf8b877aa9fbd68848daad1c48e41f7f53e5ce784a212c9","observation_id":"c0080bee-8e10-4f05-a2ee-58e566a48cbe","resolution":{"observed_at":"2026-08-07T19:25:41.295124Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.280760Z","title":null,"venue":null,"work_id":"246e2707-46a5-472a-ba69-22d483567f8a","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.703125Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:0618df439579da5ff192e1a15d3b0fce95ec1c6810747230a6454d6c7b2b2588","observation_id":"c117f9d7-42ab-4bc9-9431-ac1444c38dd8","resolution":{"observed_at":"2026-08-07T19:25:41.284436Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.269583Z","title":null,"venue":null,"work_id":"5adb7c12-a698-4872-a98b-adeea3b557b2","year":2004},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.706788Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:e89c191f4ed76f50340af37a259855c12fb136a7f70dce469fd67932a75d7179","observation_id":"51c48330-c338-4d62-84d2-e0583364eade","resolution":{"observed_at":"2026-08-07T19:25:41.273884Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.258559Z","title":null,"venue":null,"work_id":"69bbe8db-89db-4a3d-8635-0aabe2b467de","year":null},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.711156Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:4209ec7027eff037ac29c5c1ca62cf21aacc55dddc03b4d79229aa22cfe12e95","observation_id":"3cb5261b-2a50-483d-a4dc-68612250e66f","resolution":{"observed_at":"2026-08-07T19:25:41.262084Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-07T19:25:40.714836Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.714836Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:98ae392d399151de40f8a940bce19ee64c89a61d4d15ba1c7701e80bf9791a74","observation_id":"ab139d4d-7a44-4e49-bbfb-eb2e48e2e244","resolution":{"observed_at":"2026-08-07T19:25:40.714836Z","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":"2026-08-07T19:25:41.247676Z","title":null,"venue":null,"work_id":"fc997d81-ef93-4d19-b928-2a0e18f98f46","year":2016},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.718949Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:0db0038d810405e34a1a3255ba5c848f4c77cab7f97325463ba214429170a4b5","observation_id":"d344643c-f3ed-407f-93b3-1dce4146efeb","resolution":{"observed_at":"2026-08-07T19:25:41.251253Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.235250Z","title":null,"venue":null,"work_id":"8cb2d27c-4beb-4925-8126-9fb05a37a4af","year":2021},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.722633Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:d21bb745d7d54a6df117f9646db62461a612f832a99b14dd17690a44999c2f34","observation_id":"96112870-c8b0-48e0-877e-d4c3169daa42","resolution":{"observed_at":"2026-08-07T19:25:41.240124Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.224675Z","title":null,"venue":null,"work_id":"3ec9e178-11b5-49fb-a1c6-352f3e80d2a2","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.726357Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:27bb404e5d0dd04e72eadd412a009a125938fc8e679714762a0cccc227d81604","observation_id":"03b0c80d-cfe2-4482-baea-a80f5e0389b7","resolution":{"observed_at":"2026-08-07T19:25:41.228191Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07874","last_updated":"2017-11-25T03:53:32Z","snapshot_observed_at":"2026-08-14T14:38:48.107524Z","submitted_at":"2017-05-22T17:38:10Z","title":"A Unified Approach to Interpreting Model Predictions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07874","snapshot_observed_at":"2026-08-07T19:25:40.729715Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.729715Z"},"links":{"cited_paper":"/paper/1705.07874","citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:c5e9fe9b82d54d052d1cb1bcab9f0206be3f2e66da7348fc04274470bf7cba89","observation_id":"d84f4a06-ccbf-4bc3-84bd-70a1192bcbbb","resolution":{"observed_at":"2026-08-07T19:25:40.729715Z","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":"2026-08-07T19:25:41.214274Z","title":null,"venue":null,"work_id":"cad16cd0-3d8c-4036-9d2d-7a33b0c08546","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.733821Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:e40b3f3f6edfe91c7994143d0c92165f20cf8cef44a8b8dcc79ad59a3aa40bee","observation_id":"d7832d50-5623-4141-9e34-04e92fa07963","resolution":{"observed_at":"2026-08-07T19:25:41.217922Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:40.737492Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.737492Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:b0bed756aeda5100dc396ca689a9c61c54c36f7d1eaea608c6fe0edb83530029","observation_id":"7d80fea2-6822-4d5f-aef9-f0612ce48c25","resolution":{"observed_at":"2026-08-07T19:25:40.737492Z","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":"2026-08-07T19:25:41.196138Z","title":null,"venue":null,"work_id":"9ae7414f-1e4e-45da-a5b0-78e048c57d49","year":2023},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.741027Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:bc600b7bf24ef250e06f17b315daf2545ff2a70304a0fd71b83eda1e15be2dc4","observation_id":"19cc989a-29fb-457f-b458-7fbe2ccff9e2","resolution":{"observed_at":"2026-08-07T19:25:41.200848Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:40.745131Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.745131Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:9b2a57a85160b2b5125b7700092f6b82f42590dbf6a6625f1bab2211eb5d4be8","observation_id":"1205a33e-684e-4c03-a38f-caf13b5a3243","resolution":{"observed_at":"2026-08-07T19:25:40.745131Z","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":"2026-08-07T19:25:41.179189Z","title":null,"venue":null,"work_id":"7815e292-7933-44cb-8feb-39f2994296e7","year":null},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.752367Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:2afe8a17889d7d31ab10065985172dda02b418da17abe76adf48c36ffec4af96","observation_id":"34fa652e-69e4-4016-b45b-cf9403cc97fc","resolution":{"observed_at":"2026-08-07T19:25:41.182635Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:40.758636Z","title":"Why should i trust you?","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.758636Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:26a8c05980de8707ecc6535d3cfdc3c6edc65d8f7f99c358a6166fed773277a4","observation_id":"bf14710a-a4c9-4971-b14a-c39c89c20082","resolution":{"observed_at":"2026-08-07T19:25:40.758636Z","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":"10.1007/978-3-540-89689-0_33","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T00:03:45.533247Z","title":null,"venue":"Lecture notes in computer science","work_id":"c6f171ca-980c-46f3-be41-f502574de135","year":2008},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.762229Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:520278ee392e60e8e2f483b2f182f8c09d26243f2db6b5e4fd78239ff5256189","observation_id":"5a7291ff-927b-41db-a700-89874357bdb6","resolution":{"observed_at":"2026-08-07T19:25:40.829903Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.150525Z","title":null,"venue":null,"work_id":"15001419-05a1-4e67-8dd2-08c1d482d095","year":2004},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.766273Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:d11ddaeb8879e3247030f1f05e7cb7e30b0bd9c50ed42bfaef112bc234db1b3f","observation_id":"f488fc21-302b-456a-b1ab-7573d5c99564","resolution":{"observed_at":"2026-08-07T19:25:41.154189Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.139542Z","title":null,"venue":null,"work_id":"81c6b4d0-42d3-4484-9e8e-edfb29bbf108","year":2021},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.769410Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:fa2173e624069d62bc37d27621f12c93e56b1a1b57db56b165a20a071b3bd45e","observation_id":"63d124f7-87a9-4416-9f8b-cc5462acc5d5","resolution":{"observed_at":"2026-08-07T19:25:41.143314Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.128381Z","title":null,"venue":null,"work_id":"73387184-4cbd-4574-a557-e2ec0d5ceb24","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.772784Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:130edab5a2612177ac64c8b2930f806db63bfcb5fb4604ea3353e21ae1d00e7f","observation_id":"9f63b7f2-5cad-40c0-bfdf-d7832701f7a2","resolution":{"observed_at":"2026-08-07T19:25:41.131793Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.117962Z","title":null,"venue":null,"work_id":"e054a0c1-8223-4019-9821-80ddabc2ea00","year":2017},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.776491Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:15af50b8db6d2f7776fcaeb2f01568ad1eca2f9448d205076d32650b5b5bb383","observation_id":"f5845863-43d9-4580-b75d-9ee377a20f2f","resolution":{"observed_at":"2026-08-07T19:25:41.121440Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.107313Z","title":"Zheng, Jacqueline A","venue":null,"work_id":"6b9c17a1-34ec-458f-bad2-c85ea55923b3","year":2023},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.779999Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:0455cb3cdaec782b9cb29d65a972a81bc863b55ec2ffbcde1d7d44c96faa7357","observation_id":"690a9cb1-9e9c-429d-89dd-26889ed98e5a","resolution":{"observed_at":"2026-08-07T19:25:41.110633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.096416Z","title":null,"venue":null,"work_id":"42a4acf0-3332-4714-9e5d-1cfe1a4e78c9","year":2022},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.783778Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:d04c4db4216747863ab7dbc05bd0c79cae39c16c05115bd49547fb6b916606e7","observation_id":"f6f9019a-03a4-4db8-8408-06903f28edb8","resolution":{"observed_at":"2026-08-07T19:25:41.100032Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:41.085875Z","title":null,"venue":null,"work_id":"2b2650f3-b284-4da7-a031-a1881d80dedd","year":2023},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.787666Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:cc08ebe5b76aa5c4c8e86522e96bcd4587fda99f5f681215269c33a454943c46","observation_id":"29aa2648-54f7-4bba-a8c5-ee30c609e293","resolution":{"observed_at":"2026-08-07T19:25:41.089293Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T19:25:40.791200Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.791200Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:abe7d0dd5508f0259f70be20ea799ebe12472e559d9507767e163eacdc1f0b60","observation_id":"b5880cea-0bfa-427c-b047-b1c34dc1205d","resolution":{"observed_at":"2026-08-07T19:25:40.791200Z","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":"2026-08-07T19:25:41.068636Z","title":null,"venue":null,"work_id":"10864259-dbb3-43d6-b88d-6ba64a7040bc","year":1977},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.794877Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:11b829bcfdd94be5441ce1c3cd88260f938abdc4db730827e54084d2eedc6fdc","observation_id":"b8632497-335c-440e-a8ce-edcd0669852c","resolution":{"observed_at":"2026-08-07T19:25:41.072447Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05287","last_updated":"2020-02-14T01:47:35Z","snapshot_observed_at":"2026-08-10T23:39:48.575656Z","submitted_at":"2020-02-13T00:03:09Z","title":"Geom-GCN: Geometric Graph Convolutional Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.05287","snapshot_observed_at":"2026-08-07T19:25:40.748982Z","title":"arXiv preprint arXiv:2002.05287 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.748982Z"},"links":{"cited_paper":"/paper/2002.05287","citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:27ae1ccad8a811fe4a94d8d5f59259b404b2a5a4a7d724d9596edee5d83d3e42","observation_id":"cf41b542-a70e-4734-8730-aa6690ee28f6","resolution":{"observed_at":"2026-08-07T19:25:40.748982Z","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":"2026-08-07T19:25:41.168896Z","title":null,"venue":null,"work_id":"4fe86fde-9097-4cee-8603-e96db99e6c77","year":2023},"citing_paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T19:25:40.755606Z"},"links":{"citing_paper":"/paper/2502.10111"},"observation_digest":"sha256:dc3b8019fb46cfed6adb3a5bc2732b46429b158a6ebd08f32d8f975c21f1a54f","observation_id":"ed0280c5-3f73-411e-99cc-21d908b2c416","resolution":{"observed_at":"2026-08-07T19:25:41.172520Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.10111","last_updated":"2025-02-14T12:17:24Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T01:43:43.689479Z","submitted_at":"2025-02-14T12:17:24Z","title":"COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":3,"verified_fuzzy":1},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2502.10111."}