{"as_of":"2026-08-13T06:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a0480ccbe0847a182ea6271b7eb54a7b9b1a2d30c9744e2b8a1b540bded34a5f","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T23:15:52.376752Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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":"2026-07-14T02:33:34.084111Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04224","snapshot_observed_at":"2026-07-14T02:33:34.084111Z","title":"Provably robust explainable graph neural networks against graph perturbation attacks, 2025 c","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11871","last_updated":"2026-07-13T17:55:19Z","snapshot_observed_at":"2026-08-05T16:49:44.432361Z","submitted_at":"2026-07-13T17:55:19Z","title":"Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias","version":1},"reference_index":157,"source":"arxiv_source","source_observed_at":"2026-07-14T02:33:34.084111Z"},"links":{"cited_paper":"/paper/2502.04224","citing_paper":"/paper/2607.11871"},"observation_digest":"sha256:8eb87da29f10781cb103e306e6095d41b3b876f5bae517104bd7d14a3d8a3460","observation_id":"4d01c195-8e28-4213-ba51-307097b3a7cb","resolution":{"observed_at":"2026-07-14T02:33:34.084111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.04224/citation-record","integrity":"/paper/2502.04224/integrity","json":"/paper/2502.04224/citation-record.json","paper":"/paper/2502.04224"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T23:15:52.505480Z","title":null,"venue":null,"work_id":"a5c405d9-ecc6-43d4-b72a-6d2d73bb1a4b","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.359246Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:54b2d90a02a54f1ca81e7569dd6f73715c57f4d2cc139e525faeeea2bd85bf0a","observation_id":"4c3a5604-c95d-407f-a492-0937c93a67bc","resolution":{"observed_at":"2026-08-08T23:15:52.510125Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.475672Z","title":"For Refine, we set its gamma parameter as 1, beta parameter as 1 and tau parameter as 0.1","venue":null,"work_id":"32c18b34-f3d7-489a-b550-77438951ae0e","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.367807Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:0c24a8a937beb68e2e529cc258f00591573d66878eb247f20cf3a31fe3d3011a","observation_id":"6701316f-bb96-4b54-b91e-bae826f57952","resolution":{"observed_at":"2026-08-08T23:15:52.480428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.594669Z","title":"Certified robustness of graph convolution networks for graph classification under topological attacks","venue":null,"work_id":"752aaa2a-34bf-4aa1-8854-321af0c1dac0","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.323786Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:4681438cbdad49c6e12ca02b32b2c362a2c40a930bc8508baa99442cc1a2793f","observation_id":"3c24abdb-34db-44cb-8d61-e18b084babf1","resolution":{"observed_at":"2026-08-08T23:15:52.599519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12105","last_updated":"2019-10-17T23:09:36Z","snapshot_observed_at":"2026-08-13T05:53:47.281170Z","submitted_at":"2019-05-28T21:49:40Z","title":"Certifiably Robust Interpretation in Deep Learning","version":3},"cited_work":{"arxiv_id":"1905.12105","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.12105","snapshot_observed_at":"2026-08-08T23:15:52.411636Z","title":"Certifiably Robust Interpretation in Deep Learning","venue":"cs.LG","work_id":"dd4a2ae1-ebcc-4769-b0a6-79e6926a15c6","year":2019},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.333498Z"},"links":{"cited_paper":"/paper/1905.12105","citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:c1407b4d8da6220c63fbcf18fb3b5161be1c3bf376fceb7e0e697c492ad0ec13","observation_id":"3bf99a70-8722-4885-8e7e-cacb32c42430","resolution":{"observed_at":"2026-08-08T23:15:52.418544Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.565535Z","title":"Explainability methods for graph convolutional neural networks","venue":null,"work_id":"61adef26-eaf9-41c7-950a-c283f90ea563","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.338923Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:72210e2c7a240379f9be5ebf5ec6039352db69ee45bf45cc6fead0a501033378","observation_id":"e7f18ee9-eeb2-4dd1-9f1f-7b5c3ad5b39f","resolution":{"observed_at":"2026-08-08T23:15:52.570021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.551242Z","title":"Reinforcement learning enhanced explainer for graph neural networks","venue":null,"work_id":"7bec7727-8989-4b0f-a020-18c5552debd0","year":2021},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.344182Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:51a1e0d8685a33481a2a79d32c6c09edcf6dc34b12829d3dd111e866463c7b10","observation_id":"13ff4dd8-b4b1-457e-86f9-d4ec112c8062","resolution":{"observed_at":"2026-08-08T23:15:52.556034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.536944Z","title":"Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking","venue":null,"work_id":"4992c194-a9e4-4e75-b9ac-bb1e24a03093","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.348648Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:3adda8a3a62539e2eaf5009b92b0f89130fc63f9be1ee78b315ef8eebffc63aa","observation_id":"8a88358f-e1b0-466a-935b-4827a33fe9d0","resolution":{"observed_at":"2026-08-08T23:15:52.541736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.490557Z","title":"the classifier: Mf = ⌊ ny −nb+I(y<b)−1 2 ⌋","venue":null,"work_id":"f4c38dc1-723c-4c01-8e1e-f3a95f85a909","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.363517Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:15f7c0de4937718ddc70249b49c1020912ace9ba1e61247ba11325399aa3a159","observation_id":"c274b62e-0556-4eaa-a052-4e24ab5069dd","resolution":{"observed_at":"2026-08-08T23:15:52.495579Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.461222Z","title":"Std” is the Standard Deviation of the explanation accuracy on test data across the 5 runs, and “Change Rate","venue":null,"work_id":"a378c537-231a-472f-9802-0286c3fb4e02","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.372346Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:ceea05cb7732f3d5792e5aa2fe3a25ae234d4f7139440f942ffeec296e527580","observation_id":"6782bbd9-6485-479b-a9e6-7c4b495705be","resolution":{"observed_at":"2026-08-08T23:15:52.466033Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.446064Z","title":"This ranges from the classic GSAGE with LSTM to modern graph transformers (Kreuzer et al., 2021; Zhu et al., 2023)","venue":null,"work_id":"4a0b71e1-9f9d-464a-a9f0-e272cbd2f2b5","year":2021},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.376752Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:c66b4b8269b6525615fe6bd9a6f2007076336fae14b81b2f039d74fd50cc6550","observation_id":"2e818aa5-6c02-4f58-bc7b-d2998f6edf35","resolution":{"observed_at":"2026-08-08T23:15:52.450955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.521123Z","title":"Note that the complete graph GC is fixed and all subgraphs built from it are never affected","venue":null,"work_id":"5d2c3b12-1f1e-4239-bda6-bc248d73d089","year":2025},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.353975Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:fc4fb33a6648eaa90e57aa98fcc97b9c48cc627e0f4211c9255d3372109604e5","observation_id":"a7b8df88-d9bd-4624-b66f-0297b157ea2e","resolution":{"observed_at":"2026-08-08T23:15:52.526319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.02918","last_updated":"2019-06-15T07:40:33Z","snapshot_observed_at":"2026-08-06T16:43:23.424242Z","submitted_at":"2019-02-08T02:08:19Z","title":"Certified Adversarial Robustness via Randomized Smoothing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.02918","snapshot_observed_at":"2026-08-08T23:15:52.313141Z","title":"Certified adversarial robustness via randomized smoothing","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.313141Z"},"links":{"cited_paper":"/paper/1902.02918","citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:8fd30ac8a7a71d93d19b377f3cd73d83baa5bec25b03191a8ccae44fe7f4f65d","observation_id":"e9ab4081-8cf7-4dbe-8ddd-85978333ca9f","resolution":{"observed_at":"2026-08-08T23:15:52.313141Z","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":"2026-08-08T23:15:52.579746Z","title":"Certified robustness to adversarial examples with differential privacy","venue":null,"work_id":"02796011-ebb9-4de6-85a9-8698e26daee6","year":2019},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.328549Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:2881b6a4c2dda19bdb19cbd9eb6fd91a65efa1c6e67fb1bb1e85da4a0e322554","observation_id":"6eade07e-62c5-4fad-902f-05bbee90d8a7","resolution":{"observed_at":"2026-08-08T23:15:52.584922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-08T23:15:52.609809Z","title":"Certified robustness of commu- nity detection against adversarial structural perturbation via randomized smoothing","venue":null,"work_id":"f6985194-f6ae-4229-ad76-294f03c9ff70","year":2020},"citing_paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-08T23:15:52.319062Z"},"links":{"citing_paper":"/paper/2502.04224"},"observation_digest":"sha256:8afcbf1e56c784ddf74b3e8908b4da731a0935375b7a7bcc04c842e0301215df","observation_id":"8fe9c02a-4065-43df-af86-dd387b63465b","resolution":{"observed_at":"2026-08-08T23:15:52.615427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04224","last_updated":"2025-02-06T17:07:52Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T23:03:36.272014Z","submitted_at":"2025-02-06T17:07:52Z","title":"Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":2,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":14},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2502.04224."}