{"as_of":"2026-08-16T20:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ce7023ecf878f893441f6d52b8bb14541abf494d0525a1d33560d8f41087b5a2","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":"2026-08-15T20:30:54.956706Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2505.12894/citation-record","integrity":"/paper/2505.12894/integrity","json":"/paper/2505.12894/citation-record.json","paper":"/paper/2505.12894"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:30:55.500009Z","title":"EPA: Exoneration and prominence based age for infection source identification","venue":null,"work_id":"b52d12f7-e3cf-45f4-aed5-d549673fcca6","year":2019},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.799483Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:0f1436ef7d6da802e26bf60bb1aabff3faf75300ed6092ed4e0b58fd88ad5d2b","observation_id":"d236f0fb-eebb-4b0c-977b-3a0a6731a553","resolution":{"observed_at":"2026-08-15T20:30:55.504881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.419939Z","title":"On visual similarity based 3d model retrieval","venue":null,"work_id":"fedabbd7-3d95-4991-9ccc-ad961ecdd607","year":2003},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.828813Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:2200c9105052a40740c3fd1a2e94f571dfdda15ee45f292f3fcc8651fd64c501","observation_id":"1d0793c9-89e0-4608-a444-d43efaa63ff7","resolution":{"observed_at":"2026-08-15T20:30:55.424641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.405960Z","title":"Efficient source detection in incomplete networks via sensor deployment and source approaching.IEEE Transactions on Information Forensics and Security,","venue":null,"work_id":"b6589dcf-2dfd-48ee-a73d-b5d595ab815c","year":2025},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.833428Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:888007fae2575776e52882d585d0bd055ff60b305822f27bfb58313acd4935fe","observation_id":"f2e77ffe-d9c2-4367-aac5-58a431d87eea","resolution":{"observed_at":"2026-08-15T20:30:55.410908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.364867Z","title":"Social contagion models on hypergraphs.Physical Review Research, 2(2):023032,","venue":null,"work_id":"3ef1ee49-4cae-4f1e-be78-72585b039708","year":2020},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.847219Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:6d4f430fd9eda6b5368b7f558a36f4aacf5c646c4539af120ed0b74f0b2fed61","observation_id":"b769edb3-0f89-4a6c-923c-635feb5a07cb","resolution":{"observed_at":"2026-08-15T20:30:55.369485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.351257Z","title":"Multiple ru- mor source detection with graph convolutional networks","venue":null,"work_id":"dd03cef8-87a0-4680-9d18-35a238ef7c0b","year":2019},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.851398Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:fb238a7eaff3c3020180afbd74b1c2f3b3ef2a51998cdff52a42dc5ba23a8e41","observation_id":"a6035bdc-9a1a-4d87-80c1-f3b3b43f961f","resolution":{"observed_at":"2026-08-15T20:30:55.355907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.337318Z","title":"Wavefront- based multiple rumor sources identification by multi-task learning.IEEE Transactions on Emerging Topics in Com- putational Intelligence, 6(5):1068–1078,","venue":null,"work_id":"39a57f39-a3ff-4af3-b2a8-a22b090b40bd","year":2022},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.855884Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:41bd64620f8de2d19aa3d721c0123eb1089b40cda1866d003bdee02935373b4c","observation_id":"8865c4cc-94b7-47c1-a39c-af67933d876c","resolution":{"observed_at":"2026-08-15T20:30:55.341778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.322570Z","title":"Unveiling implicit deceptive patterns in multi-modal fake news via neuro-symbolic reasoning","venue":null,"work_id":"0ff5937e-188c-4629-9d0e-71d5e8078323","year":2024},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.859997Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:65822bdcd30a1d56330a74dc4e0385d4906583688db5318cd409e0bfee386b33","observation_id":"ea73ff60-d67d-49d7-9072-b2a0ffaf25c3","resolution":{"observed_at":"2026-08-15T20:30:55.327462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.308301Z","title":"Hypergraph neural networks","venue":null,"work_id":"5da1901a-7120-48b6-b630-1394b4f261c7","year":2019},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.864168Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:d75918c6e61c145324af1546e56ffd5836ab48229d964dc689543849ac98fb74","observation_id":"5279942c-4784-4a9e-8951-e329a71f0c41","resolution":{"observed_at":"2026-08-15T20:30:55.312646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.293269Z","title":"HGNN+: General hypergraph neural networks","venue":null,"work_id":"152d5122-a3bd-4fff-a6b7-696ef722198a","year":2022},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.868108Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:9ac304128920e1a4d30a78d6a190a78b639d7ca941c3538bf5e2d330a305c13e","observation_id":"794c0feb-df7e-451e-bbb8-13aabb13487c","resolution":{"observed_at":"2026-08-15T20:30:55.298722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.278171Z","title":"Unignn: a unified framework for graph and hypergraph neural net- works","venue":null,"work_id":"eb4780cd-8a56-43e1-be3a-ddc285dc8097","year":2021},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.872201Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:e8c58555a317793465e9a141a81e6a5f8ba1778f4a109103255c1b1dc168d7cf","observation_id":"3b99e902-4ea9-4638-ae59-8ea0e8bc45a5","resolution":{"observed_at":"2026-08-15T20:30:55.283283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.264176Z","title":"Enhancing multi-scale diffusion prediction via sequential hypergraphs and adver- sarial learning","venue":null,"work_id":"76c1e608-39c2-4f63-a5b1-e892975cabdf","year":2024},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.876789Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:3b7764492ec155328bcb4b807f72ec8d9324224cb10a8b9e4574dfc65d5be8bb","observation_id":"179c6a5f-c6b1-4d4f-a3b4-50f74c5dd011","resolution":{"observed_at":"2026-08-15T20:30:55.268374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.249585Z","title":"Local-global de- fense against unsupervised adversarial attacks on graphs","venue":null,"work_id":"154526f6-afd5-4227-9021-72ea4fe8f76b","year":2023},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.881727Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:3276b43adc0f6b3c381d426993bf5b38559b1df9d561c94f28fd222767ff8b54","observation_id":"0747e321-2693-4ede-bc59-ea1598649c58","resolution":{"observed_at":"2026-08-15T20:30:55.254447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.204883Z","title":"Source localization of graph diffusion via variational autoencoders for graph inverse problems","venue":null,"work_id":"6ae6ceeb-e5d4-46f4-91bb-fd65fa6c8fe5","year":2022},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.895406Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:6ba491bfeb5824c63a1231c00e6afddba809fbc52d89d27735ba2cab15e303cf","observation_id":"daec2bc8-eb3e-4a92-87f7-8d032683f13f","resolution":{"observed_at":"2026-08-15T20:30:55.209707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.189766Z","title":"Locating the source of diffusion in large-scale networks.Physical Review Letters, 109(6):068702,","venue":null,"work_id":"84d982c7-40ab-486b-9d30-a1c9804240d1","year":2012},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.899664Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:79d13ae9fd52f55ee2de6c78b8230e0b94292801ad7741b2cd25f81c68c94835","observation_id":"9c02d3e4-3b92-4a63-ae2e-a77ea1b20bc7","resolution":{"observed_at":"2026-08-15T20:30:55.194691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.159645Z","title":"Rumors in a network: Who’s the culprit?IEEE Transac- tions on Information Theory, 57(8):5163–5181,","venue":null,"work_id":"4a0fdbf8-2543-469a-b42c-036f0ac012a1","year":2011},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.908714Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:03852544838c9979f261001b723fb16608ca31c2ec132f9e5081d100c57f4c6e","observation_id":"893376fb-d244-42c4-8c57-eb1fb318cf6f","resolution":{"observed_at":"2026-08-15T20:30:55.165017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.145272Z","title":"Graph attention networks","venue":null,"work_id":"95df5fa5-207d-41f6-bb5d-37855f68ecf8","year":2017},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.917470Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:ad856a1d96a2b85af51e56feba8673a40c297776539082ec16fcb3792e4be59c","observation_id":"dc7a1b09-a765-4f39-9c84-072e2fb9cc1e","resolution":{"observed_at":"2026-08-15T20:30:55.149661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.129175Z","title":"Multiple source detection without knowing the underlying propagation model","venue":null,"work_id":"8dfaa255-01f6-4696-9243-2f0bd93cc093","year":2017},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.921932Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:b2575692d58e4f551885cc6aebfc20a94d108b887c93665c1e48c61b509ff549","observation_id":"870eeb6d-0e08-4a85-acba-da560777d952","resolution":{"observed_at":"2026-08-15T20:30:55.134382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.113316Z","title":"An invertible graph diffusion neural network for source localization","venue":null,"work_id":"ac6aad2d-1621-40ee-862e-438c0ecaf36d","year":2022},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.926706Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:87fd8ada0935ba7c6fc053e2b256bc183ec6a664e5fa601c8ec93c8fcbd50a9c","observation_id":"8a554e09-b6ec-4db4-8203-00713d8d3440","resolution":{"observed_at":"2026-08-15T20:30:55.117926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:54.931198Z","title":"Elevating knowledge-enhanced entity and relation- ship understanding for sarcasm detection.IEEE Transac- tions on Knowledge and Data Engineering,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.931198Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:bbbe2c8a8f3645d2367965c89d63bad582dd72317ed6f1666523f68aa6f15a0f","observation_id":"968beac8-4a2e-474c-9512-80656099d2ae","resolution":{"observed_at":"2026-08-15T20:30:54.931198Z","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":"2026-08-15T20:30:55.087767Z","title":"3d shapenets: A deep representation for volumetric shapes","venue":null,"work_id":"09fd56ad-5add-400f-87aa-44e925c4a5da","year":2015},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.935679Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:075f0830c5fedf9da3d4d6c3d22b12ac1e3bbb4321ad1a20c5693bc6a4f2f2b0","observation_id":"0171e765-c7d5-4044-bd86-ad1920d2f29e","resolution":{"observed_at":"2026-08-15T20:30:55.093058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.055278Z","title":"Locating the propaga- tion source in complex networks with a direction-induced search based gaussian estimator.Knowledge-Based Sys- tems, 195:105674,","venue":null,"work_id":"65141ff9-4905-45c7-936a-522f57967afc","year":2020},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.944103Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:799ab9e3ae2ae98ffd2e72ca0379d88b6e68ef0692f56acc268e3e741f09943c","observation_id":"9629dd07-b8ea-4132-a760-7f033d8d2b36","resolution":{"observed_at":"2026-08-15T20:30:55.060295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.039769Z","title":"Learning with hypergraphs: Clus- tering, classification, and embedding.Advances in Neural Information Processing Systems, 19,","venue":null,"work_id":"e4b634b5-93a7-4ea2-b3bf-d71600be37da","year":2006},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.948318Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:881e5a7186843323f14cd3c0268eca0d7cbf8203d388b82b47de1bc448c165b9","observation_id":"2043f2f7-9034-447b-9674-d110d9e836c8","resolution":{"observed_at":"2026-08-15T20:30:55.044576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.004080Z","title":"Locating multi-sources in social networks with a low infection rate.IEEE Transactions on Network Science and Engineering, 9(3):1853–1865, 2022","venue":null,"work_id":"9a5a9ce7-57e6-4ac2-bdc2-ff35b3d80daf","year":2022},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.956706Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:d42374a3cf5bb47e9cfdcc3b6db86733fa2f3adab9bc43c1544d595e73e854df","observation_id":"cc71d145-5daf-44d6-864f-59af586c1765","resolution":{"observed_at":"2026-08-15T20:30:55.011431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.022561Z","title":"Catch’em all: Locating multiple diffusion sources in net- works with partial observations","venue":null,"work_id":"c4a49177-5f86-46d7-8e7a-1484aaa291ae","year":2017},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.952429Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:aaea052ec178a6060f306d08906a6f6b944e49ce17974329fe2d7f98ee632746","observation_id":"6e8d6536-4365-45a7-a8f7-118f16e88218","resolution":{"observed_at":"2026-08-15T20:30:55.028241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.462182Z","title":"Hypergraph convolution and hypergraph attention","venue":null,"work_id":"4720cdff-481d-4619-8d89-d962e1f9c5b2","year":2021},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.813764Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:72f2143bf6504c46728dd1137c775dbc5feb2d1e0157363ab291afc9cbe7dc74","observation_id":"a8f718ca-2e0d-4f3d-8e73-926f3a17bdba","resolution":{"observed_at":"2026-08-15T20:30:55.466531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11913","last_updated":"2020-06-27T04:38:54Z","snapshot_observed_at":"2026-08-16T04:21:05.831287Z","submitted_at":"2020-06-21T21:12:44Z","title":"Finding Patient Zero: Learning Contagion Source with Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11913","snapshot_observed_at":"2026-08-15T20:30:54.912834Z","title":"Finding patient zero: Learn- ing contagion source with graph neural networks.arXiv preprint arXiv:2006.11913,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.912834Z"},"links":{"cited_paper":"/paper/2006.11913","citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:d6ca14f97d37a873d6e5361e7cd8e04de4b65590f036da007a9f0ef119074d35","observation_id":"b46835a8-348d-472e-b0f8-14365feb9aa4","resolution":{"observed_at":"2026-08-15T20:30:54.912834Z","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":"2026-08-15T20:30:55.175234Z","title":"Spotting culprits in epidemics: How many and which ones? In2012 IEEE 12th International Conference on Data Mining, pages 11–20","venue":null,"work_id":"70c76b46-637b-4038-b85e-ada0c60cfe88","year":2012},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.904323Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:c87e70895aa2e2d6c9acf1dbed978b0aa5d4ef7c938759f3b96f878a3115063d","observation_id":"cf005180-24b5-48eb-9d8d-3abdf37d3ef0","resolution":{"observed_at":"2026-08-15T20:30:55.179796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.070524Z","title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs","venue":null,"work_id":"52f88032-aa51-4cd5-ac47-99ddbfb822b3","year":2019},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.939923Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:b206582ef5418d86afb80b53b1dda68dee01a21196fff31a6a62fb52cb816fd2","observation_id":"5ba40ee1-2c95-4416-9ace-48d115af44d4","resolution":{"observed_at":"2026-08-15T20:30:55.075719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.219604Z","title":"Propagation source identification of infec- tious diseases with graph convolutional networks.Journal of Biomedical Informatics, 116:103720,","venue":null,"work_id":"5865ab33-07f1-447c-8729-af00ded07d3c","year":2021},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.891138Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:07cb17bc40d749dc1aab348be34577cf85164101810873e55f1e88132a0975a6","observation_id":"b4086203-b92a-4fa3-970e-760cd2caeaef","resolution":{"observed_at":"2026-08-15T20:30:55.224338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.484934Z","title":"Clustering in graphs and hypergraphs with cate- gorical edge labels","venue":null,"work_id":"ca8890ba-076f-40a8-bb01-79ce0f180efd","year":2020},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.804496Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:0f96faf38e196d8c375d5367ca1a7ead92114af1785be2fd12462574e7b75955","observation_id":"ae2d744f-14cb-49bd-88a7-6c747fec2335","resolution":{"observed_at":"2026-08-15T20:30:55.490224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:54.809065Z","title":"Uci machine learning repository,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.809065Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:a0ec713a636cbe0fa6f5ab377e8fd724b572e791cdbcf32518e6e4db1e3d5ad8","observation_id":"be72848b-66bd-4c68-8a5d-cd9ee583a601","resolution":{"observed_at":"2026-08-15T20:30:54.809065Z","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":"2026-08-15T20:30:55.448913Z","title":"Graph contrastive learning for source localization in social networks.Information Sci- ences, page 121090,","venue":null,"work_id":"fc27b5aa-e13d-4bc7-bff7-8d9266f6401f","year":2024},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.818819Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:2d2e14823f07a2ac76e75f8986244641298051e6100f1da87c45e410e8a721c8","observation_id":"b9c64cca-116f-4132-bb24-52faa6d87fa6","resolution":{"observed_at":"2026-08-15T20:30:55.453471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.378116Z","title":"Generative hypergraph clustering: From blockmodels to modularity.Science Advances, 7(28):eabh1303,","venue":null,"work_id":"89c177b3-d653-4c1b-b5ad-a8606d420241","year":2021},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.842602Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:4e7d6fcbfc0ec985dee36008653a33d89e7ab380f8b6124023773c6b1c788e8d","observation_id":"7597d936-1201-4973-ac07-8b7bafaf0f6d","resolution":{"observed_at":"2026-08-15T20:30:55.382664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.234344Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":"40d8915c-601b-40bb-9dd0-bbdfdf263dc3","year":2017},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.886212Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:7821e4211ad6f413c62b2f2d3f435806882bb0ca9a59fb52e5c5a853ca675b1a","observation_id":"2fb0bf86-dd81-4fef-bf31-8ce521d3b26b","resolution":{"observed_at":"2026-08-15T20:30:55.239387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.434450Z","title":"Networks beyond pairwise interactions: Structure and dynamics","venue":null,"work_id":"28621e59-227b-4379-8c3d-9bf627aee95b","year":2020},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.823516Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:ee76ea6720256fa460a8f5ac41087d39101a250ca73379a36de235da69559d61","observation_id":"096471b9-5a21-45a1-8b9f-34817cd49a9e","resolution":{"observed_at":"2026-08-15T20:30:55.439437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T20:30:55.391719Z","title":"You are allset: A multiset function framework for hypergraph neural networks","venue":null,"work_id":"da88944a-4697-4334-8d2a-c6a61118c54b","year":2022},"citing_paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T20:30:54.837890Z"},"links":{"citing_paper":"/paper/2505.12894"},"observation_digest":"sha256:b0625273448d3e22e8117a321e78ac37a9379474c9ca4a79bd843cffc0b2d24b","observation_id":"0847027a-c043-4b7f-94b4-849714907fb1","resolution":{"observed_at":"2026-08-15T20:30:55.396789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.12894","last_updated":"2025-06-04T12:44:45Z","latest_version":2,"primary_category":"cs.SI","snapshot_observed_at":"2026-08-15T20:21:54.373431Z","submitted_at":"2025-05-19T09:27:46Z","title":"HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":33},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.12894."}