{"as_of":"2026-08-11T15:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f94531a71a467e102c1e1f7103e5a83d4c2863dd75af262cb8f6cb15cf2df76","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:03:56.964545Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.02981/citation-record","integrity":"/paper/2501.02981/integrity","json":"/paper/2501.02981/citation-record.json","paper":"/paper/2501.02981"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.822438Z","title":"https://mbernste.github.io/posts/gcn/","venue":null,"work_id":"c8cefb32-e108-4a25-8feb-a43806612c65","year":2024},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.851998Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:f5d826d96e6b84464662a1792715e7af86f46b8533b098ffe6a98309acd323a3","observation_id":"1591a1a8-0b43-4544-9f8f-43b8fa3a0487","resolution":{"observed_at":"2026-08-10T22:03:57.825568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.13597","last_updated":"2021-11-26T16:51:37Z","snapshot_observed_at":"2026-07-06T12:12:32.184432Z","submitted_at":"2021-11-26T16:51:37Z","title":"Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.13597","snapshot_observed_at":"2026-08-10T22:03:56.857735Z","title":"arXiv preprint arXiv:2111.13597","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.857735Z"},"links":{"cited_paper":"/paper/2111.13597","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:e28513f7a549cdfca8d21187ce36ba28d51c3379df0716dc1a371dfadb5988b3","observation_id":"7fc69e7b-42ad-44a4-9194-998fb8ff59b8","resolution":{"observed_at":"2026-08-10T22:03:56.857735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05034","last_updated":"2023-09-28T03:02:57Z","snapshot_observed_at":"2026-07-06T16:04:26.461227Z","submitted_at":"2023-08-09T16:04:55Z","title":"Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.05034","snapshot_observed_at":"2026-08-10T22:03:56.863537Z","title":"IEEE Communications Letters 25, 1564–1567","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.863537Z"},"links":{"cited_paper":"/paper/2308.05034","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:e3a8436242895755e5036c519e21085648b983fea78f11a9a517dc7778f06224","observation_id":"6dae9519-a343-4ba2-82a9-6b2ebaebdb30","resolution":{"observed_at":"2026-08-10T22:03:56.863537Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.872433Z","title":"IEEE Access 7, 99508–99520","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.872433Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:7f076a6efce90f30056efb04135f8d2b652008178d8d5d0f1171e4a8ae09c3f2","observation_id":"72aea397-20ee-48ed-b317-44b25c8fe96f","resolution":{"observed_at":"2026-08-10T22:03:56.872433Z","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":"2026-08-10T22:03:57.801302Z","title":"2020 IEEE Symposium on Security and Privacy (SP) , 1172–1189URL: https://api.semanticscholar.org/CorpusID:216263050","venue":null,"work_id":"2bd36a36-64bf-4225-88fb-93051e2c2882","year":2020},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.880634Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:313f9676c103891359ed82f9aa3768ef5e84c0546362908f27edcc846236df45","observation_id":"96f5c670-5abd-4ebd-960c-cafdaea90f5e","resolution":{"observed_at":"2026-08-10T22:03:57.804076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07145","last_updated":"2021-09-08T00:22:55Z","snapshot_observed_at":"2026-08-06T20:53:44.444031Z","submitted_at":"2021-04-14T22:11:35Z","title":"FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07145","snapshot_observed_at":"2026-08-10T22:03:56.883134Z","title":"arXiv preprint arXiv:2104.07145","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.883134Z"},"links":{"cited_paper":"/paper/2104.07145","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:400ef40b725068473667f7071d98a903bdc3320a465128f530bea76fe05a8990","observation_id":"74e44756-fb79-4144-8379-0c89af83aa34","resolution":{"observed_at":"2026-08-10T22:03:56.883134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.09831","last_updated":"2023-10-15T13:27:06Z","snapshot_observed_at":"2026-07-06T16:33:22.071499Z","submitted_at":"2023-10-15T13:27:06Z","title":"MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.09831","snapshot_observed_at":"2026-08-10T22:03:56.886346Z","title":"arXiv preprint arXiv:2310.09831","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.886346Z"},"links":{"cited_paper":"/paper/2310.09831","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:d96ba05eb9001da0fb56c548e194ecf82974e42877ea39adc9940800edfc33f6","observation_id":"964b0cf9-1e81-43dc-bc54-4d73c143ca2d","resolution":{"observed_at":"2026-08-10T22:03:56.886346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10837","last_updated":"2024-06-17T15:39:21Z","snapshot_observed_at":"2026-08-03T15:45:06.572630Z","submitted_at":"2023-03-20T02:44:35Z","title":"FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10837","snapshot_observed_at":"2026-08-10T22:03:56.889141Z","title":"arXiv preprint arXiv:2303.10837","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.889141Z"},"links":{"cited_paper":"/paper/2303.10837","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:1bc796716f83d064e617ee84286a83a7a23e33ef6938ad920158fcd33e8fa705","observation_id":"778b0eb4-1e5d-43a3-9ab4-67ed8a7472dd","resolution":{"observed_at":"2026-08-10T22:03:56.889141Z","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":"2026-08-10T22:03:57.787111Z","title":null,"venue":null,"work_id":"6ddea97c-9e73-48e1-939f-ea4a2e13c59a","year":2022},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.903193Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:e40ef8fd618889fa38ee39873cdf6e3e07306d5e6ce81a6c8702429fb6141e90","observation_id":"e0082346-cfe9-4e7b-b7b9-37388f2155f4","resolution":{"observed_at":"2026-08-10T22:03:57.789674Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10586-017-1256-y","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.999925Z","title":"Cluster Computing 22, 7347–7358","venue":null,"work_id":"e23d6da5-046f-4db3-97e9-83f4130c28aa","year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.905702Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:d37828c8067316beb1330a320954ef046b3b5781eae7029395cf087dc35bf80f","observation_id":"6af3c100-6546-4c8a-bc41-619d9e8f18f2","resolution":{"observed_at":"2026-08-10T22:03:57.002595Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.908438Z","title":"2022 7th IEEE International Conference on Data Science in Cyberspace (DSC) , 498–505doi:10.1109/dsc55868.2022.00075","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.908438Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:bea1e8f1aa366e6999822e1cbb7c8e008159e9fbc242d4f62f729e09f849a543","observation_id":"314b778d-99eb-47d1-8545-28373655b27e","resolution":{"observed_at":"2026-08-10T22:03:56.908438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10637","last_updated":"2020-10-09T11:39:32Z","snapshot_observed_at":"2026-08-07T10:11:23.671536Z","submitted_at":"2020-06-18T16:06:18Z","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10637","snapshot_observed_at":"2026-08-10T22:03:56.911085Z","title":"arXiv preprint arXiv:2006.10637","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.911085Z"},"links":{"cited_paper":"/paper/2006.10637","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:fdb8011a123b0c1d08b88f9e4eae43eabf88f2b788ec451911c9871a5cdbd2d2","observation_id":"8be1fbf0-b3c7-4df3-a124-abd853e72eb3","resolution":{"observed_at":"2026-08-10T22:03:56.911085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10569","last_updated":"2023-02-11T23:23:24Z","snapshot_observed_at":"2026-08-05T16:08:04.147924Z","submitted_at":"2023-01-25T13:17:46Z","title":"Spatio-Temporal Graph Neural Networks: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.10569","snapshot_observed_at":"2026-08-10T22:03:56.913955Z","title":"arXiv preprint arXiv:2301.10569","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.913955Z"},"links":{"cited_paper":"/paper/2301.10569","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:d47e5c022dc66baf733be6d98cac686802ab8891edf37852bd33dda29309cc7d","observation_id":"b2cced7f-ecc3-4f62-bd9e-9164bf5a5bf0","resolution":{"observed_at":"2026-08-10T22:03:56.913955Z","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":"2020.29641","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.334098Z","title":"IEEE Internet of Things Journal 7, 4641–4654","venue":null,"work_id":"4fa14ce5-b121-41bc-881d-8a7fb657df29","year":2020},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.916878Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:341bb782eec12830c0038c2793c20ca98a6a486fae9b105b7b83e503d79b7ede","observation_id":"561cb616-e098-4a1b-9fb2-23980884e73a","resolution":{"observed_at":"2026-08-10T22:03:57.338139Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.779781Z","title":null,"venue":null,"work_id":"5bb0718f-0fd2-4a29-a31e-5dca838f4e3a","year":2018},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.919696Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:062ee28798287b498cb34a801c53b066d2a710ab849fb92ab8d1f7a57608b1fd","observation_id":"c99a0c82-8f91-45db-8b1f-60b067504318","resolution":{"observed_at":"2026-08-10T22:03:57.782563Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.79063","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.271343Z","title":"2016 Picture Coding Symposium (PCS) , 1–5doi: 10.1109/PCS.2016.7906393","venue":null,"work_id":"1d6e7be7-3ae8-41b2-af3c-98681f2844cc","year":2016},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.922245Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:bb4b7ec7f861032e5063b745d8ecd17d3ef5910e23e895ea57716fe3a3edaed2","observation_id":"0e47729b-ce68-4c7d-826b-99837ada86bf","resolution":{"observed_at":"2026-08-10T22:03:57.275114Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.7910/dvn/ia8uos","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.007441Z","title":"Wget Dataset","venue":null,"work_id":"4a31da91-dfac-4da7-aa31-8b59fcc9733e","year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.875017Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:fd52619dde793ad274d921b4a11cd96a7f5ef0ff0e6f30fd202940924fd5cc4f","observation_id":"e1eaa02e-844d-4163-861d-388806234e32","resolution":{"observed_at":"2026-08-10T22:03:57.010404Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.773128Z","title":null,"venue":null,"work_id":"ac55512c-561a-46cf-8f91-59eaa7020389","year":2016},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.927473Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:69b06764f389f7162a7430d6355b1c72e596bbe3af5d1503fe126f62d872aa54","observation_id":"f19424ec-f3c3-427f-a12e-7757832df59d","resolution":{"observed_at":"2026-08-10T22:03:57.775652Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-10T22:03:56.930164Z","title":"arXiv preprint arXiv:1710.10903","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.930164Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:f798f0ac44322b27ab6aeaedfcfe19564cc983f2b27b188a75d33a4fc1ce13fd","observation_id":"41562b62-b88e-4d61-8528-9fdf2a5a77e6","resolution":{"observed_at":"2026-08-10T22:03:56.930164Z","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":"2026-08-10T22:03:57.765927Z","title":null,"venue":null,"work_id":"40ade2d2-5030-48b7-be2d-ecf6268359d7","year":2015},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.932885Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:6a963293847869cbcd7f5ffb46bd0975b32b83a5426f9adc0546a4385739075c","observation_id":"a2cb9764-34bc-44a1-a171-356bf7f36949","resolution":{"observed_at":"2026-08-10T22:03:57.768727Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.758096Z","title":"1894–1903","venue":null,"work_id":"6a430a28-8157-4768-aafe-1848dac8d938","year":1903},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.935456Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:3e73e659bed598acccd036502f36d5c5524896456a23c31301c4fcf53281f17c","observation_id":"67de2142-9ccc-44fa-8c8e-2a6be3b11654","resolution":{"observed_at":"2026-08-10T22:03:57.761163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-07-06T07:05:24.565760Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-10T22:03:56.938052Z","title":"Xu, M., Dai, W., Liu, C., Gao, X., Lin, W., Qi, G.J., Xiong, H.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.938052Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:c9d59881400d896ac83bc9f1c44544bdc51e4bb477fa063849d1b3e4812bbecf","observation_id":"d513a8c4-48f9-48a3-8af2-ba464c012dcf","resolution":{"observed_at":"2026-08-10T22:03:56.938052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.02908","last_updated":"2021-03-29T09:59:36Z","snapshot_observed_at":"2026-08-05T20:31:02.244623Z","submitted_at":"2020-01-09T10:21:04Z","title":"Spatial-Temporal Transformer Networks for Traffic Flow Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.02908","snapshot_observed_at":"2026-08-10T22:03:56.940950Z","title":"arXiv preprint arXiv:2001.02908","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.940950Z"},"links":{"cited_paper":"/paper/2001.02908","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:7c9271c995bfefc051979287dd5e486915bcd5cb5e2c66a0e1f13f46488428ba","observation_id":"dcd5e0db-ac85-4924-9610-dcf5976d7a5a","resolution":{"observed_at":"2026-08-10T22:03:56.940950Z","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":"2026-08-10T22:03:57.749581Z","title":null,"venue":null,"work_id":"ae556c80-153d-41f7-82d6-cc636861866b","year":2022},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.943669Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:1785c13fae8dc27aecf67799030feeb87bc369596ef03c796dd744c1f310c70b","observation_id":"020e1ae5-2560-4b92-95a5-b5b615c3c79c","resolution":{"observed_at":"2026-08-10T22:03:57.753134Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.01340","last_updated":"2017-03-03T21:13:48Z","snapshot_observed_at":"2026-08-10T04:14:08.951523Z","submitted_at":"2017-03-03T21:13:48Z","title":"Generative Poisoning Attack Method Against Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.01340","snapshot_observed_at":"2026-08-10T22:03:56.946240Z","title":"arXiv preprint arXiv:1703.01340","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.946240Z"},"links":{"cited_paper":"/paper/1703.01340","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:42146122ec7b78d9f1803969006eb614805e820633135be06e3245d88921ab30","observation_id":"0fbaf692-ecb4-4602-8980-245992530bc6","resolution":{"observed_at":"2026-08-10T22:03:56.946240Z","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":"2026-08-10T22:03:57.742157Z","title":null,"venue":null,"work_id":"aef134e7-6d1f-4d92-bf1d-baa48fdaa989","year":2023},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.949022Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:f73855ff65cffb6f7942e01c0242f78e9cd974235a9779dd9e2dd9c716fb8d42","observation_id":"9a931d35-88c4-45f3-b13f-53e96d25ddf3","resolution":{"observed_at":"2026-08-10T22:03:57.744932Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.734954Z","title":null,"venue":null,"work_id":"3be33f55-6137-46cc-8ad8-74e7f12fb55d","year":2019},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.951683Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:0207937796e4b1490ae8911ac26705abc9691301bf6690ab5b8f15f4e6decc07","observation_id":"1be6c404-8c0a-4cea-826b-66018944d2d8","resolution":{"observed_at":"2026-08-10T22:03:57.737611Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6435.2022","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.173361Z","title":"2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) , 493–497doi:10.1109/ICBAIE56435.2022.9985933","venue":null,"work_id":"3d1e7eb0-1d5b-48c6-913c-3a9d29af1498","year":2022},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.954215Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:2a9de860a4f96f687bf382aa6a3ff6764af0cb54be12e82020f061f784bc7262","observation_id":"ca74492b-c9bd-420c-a087-8381f98e0dec","resolution":{"observed_at":"2026-08-10T22:03:57.177536Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.727753Z","title":null,"venue":null,"work_id":"3cc3e78d-1a3e-487b-82ff-bca06ddc6d51","year":2022},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.956645Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:4cb53139544c673a054163e9c2dab63d26c3413c96bc44f6e81b2d3ebe5dfc00","observation_id":"5a6fc0fb-30dc-4ff9-ae8a-b0e95d6b443f","resolution":{"observed_at":"2026-08-10T22:03:57.730379Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.30762","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.105340Z","title":"IEEE Transactions on Information Forensics and Security 16, 3312–3325","venue":null,"work_id":"e404d481-4458-4e65-9744-ae44d98a3ea3","year":2021},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.959218Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:c4c32625cd4954575ad8583c1c1858e441a1951ee41e132d8d6c9f90970fc09a","observation_id":"64760957-6e54-4e48-8df6-8559b60cc1cf","resolution":{"observed_at":"2026-08-10T22:03:57.110364Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.720388Z","title":null,"venue":null,"work_id":"e59ea810-0b7f-4cd1-a3a6-379b013f7e19","year":2018},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.962079Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:4a1f6ec1d0858d69a80a0679d5e2c5c50cc80bda06aad55919afc14ed9dfb0f5","observation_id":"d1d6f56e-874c-4cec-b634-5b3cc97f2be3","resolution":{"observed_at":"2026-08-10T22:03:57.722981Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.future.2020","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.982270Z","title":"Future Gener","venue":null,"work_id":"98d75dcc-a314-4ea9-aa0e-5ecf769200f2","year":2020},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.964545Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:2e303347c4ebd19e20c9a0b9534ac479082ea3d35d30bb7d3bc73f3bd681fb96","observation_id":"800ea0c7-e237-408e-b79e-4b3d61f73b15","resolution":{"observed_at":"2026-08-10T22:03:56.986196Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.10697","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.654993Z","title":"Bilot, T., El Madhoun, N., Al Agha, K., Zouaoui, A.,","venue":null,"work_id":"96e89fd6-6d2b-41f3-b9a3-1f9a9614664c","year":2019},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.854879Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:34faeaac679f11dbe9c317905574da30c0a4e8ecb58debaaf5fba54d0065b5ff","observation_id":"8a94c2b3-72d0-434a-8a5e-b9b8bf88c333","resolution":{"observed_at":"2026-08-10T22:03:57.658947Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.794207Z","title":null,"venue":null,"work_id":"1b766c26-6f09-4bf9-b7e8-4839ecbd5b9e","year":2007},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.894396Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:f4ff0e8cd571cdaf17339bacf7f45395ea7edf598d3e0cf405ddbdd6e3d31394","observation_id":"4a16d382-0568-4d2c-a8ef-5dc93ab29b35","resolution":{"observed_at":"2026-08-10T22:03:57.796818Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.808584Z","title":null,"venue":null,"work_id":"545a3b5b-d8f0-44c4-8423-2e6269f92398","year":2010},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.869643Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:a85555187bda2b06e92e720e4ac3fdb9039d60c5a4cd1de1996aeaad31297052","observation_id":"f713f199-0595-4408-a1a7-fc96950d1510","resolution":{"observed_at":"2026-08-10T22:03:57.811101Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1406.1078","last_updated":"2014-09-03T00:25:02Z","snapshot_observed_at":"2026-07-06T03:45:28.546418Z","submitted_at":"2014-06-03T17:47:08Z","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.1078","snapshot_observed_at":"2026-08-10T22:03:56.866651Z","title":"arXiv preprint arXiv:1406.1078","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.866651Z"},"links":{"cited_paper":"/paper/1406.1078","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:0a3526bb2d494741bd3e9cf3e8de2bb6fae3e3ebd0904495dc5c41cd722432b8","observation_id":"6f7d41a3-b6bd-4832-b43e-f27efa34a7e9","resolution":{"observed_at":"2026-08-10T22:03:56.866651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05493","last_updated":"2017-09-22T21:36:00Z","snapshot_observed_at":"2026-07-06T04:36:48.493556Z","submitted_at":"2015-11-17T18:10:12Z","title":"Gated Graph Sequence Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05493","snapshot_observed_at":"2026-08-10T22:03:56.899852Z","title":"arXiv preprint arXiv:1511.05493","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.899852Z"},"links":{"cited_paper":"/paper/1511.05493","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:fa2fa225334854a6410d4396b44f3f375526895789f2cedba75f2f9456e499fd","observation_id":"8646a8a0-8ed4-4021-8626-e7b90a6bc4f9","resolution":{"observed_at":"2026-08-10T22:03:56.899852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","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":"2026-08-10T22:03:56.891935Z","title":"https://tkipf.github.io/ graph-convolutional-networks/","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.891935Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:4d70aabc8e515eb0bec88fa7a44f730c2195f5cc40e76b7a7ba7b18b6b5e5d8b","observation_id":"00e1df0b-0d6a-4bc1-a45d-503f4a1a897b","resolution":{"observed_at":"2026-08-10T22:03:56.891935Z","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":"10.14257/ijhit.2017.10.7.01","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.991614Z","title":"International Journal of Hybrid Information Technology 10, 1–10","venue":null,"work_id":"49f0c52e-036f-43d4-8cdb-ee443601fcbb","year":2017},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.924883Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:5dbe89337b1db54d8cc9cc2a9a816aeb2f7c18b394a6ba568309ef90016f267b","observation_id":"8940407b-501b-4611-aad6-1ff6a337cc08","resolution":{"observed_at":"2026-08-10T22:03:56.995122Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.897195Z","title":"IEEE Transactions on Signal Processing 67, 97–109","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.897195Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:d9384d40e30391aae658c371b3630ebc7d16cdf11f21512252bf538326c71913","observation_id":"95eba03e-063b-4e1a-b5da-39b140607419","resolution":{"observed_at":"2026-08-10T22:03:56.897195Z","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":"2026-08-10T22:03:57.815408Z","title":null,"venue":null,"work_id":"eb2eb24e-d103-4070-bf0f-94aa57d42153","year":2019},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.860820Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:36265e22661a3698acbf6371c66e3a759d2c054f9026db4e831c2d9ec4571633","observation_id":"5a59e8a3-b7c5-41ce-97c6-35b41a1c4444","resolution":{"observed_at":"2026-08-10T22:03:57.818049Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.01525","last_updated":"2020-01-14T12:26:28Z","snapshot_observed_at":"2026-08-09T05:50:05.820033Z","submitted_at":"2020-01-06T12:42:51Z","title":"UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.01525","snapshot_observed_at":"2026-08-10T22:03:56.877656Z","title":"arXiv preprint arXiv:2001.01525","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.877656Z"},"links":{"cited_paper":"/paper/2001.01525","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:013eaa70894b7f84ffd130d361d3154a2a6b7db62757f640301c2db2c4fbc469","observation_id":"7e380d65-5964-4270-b7b5-d58ce0c9c275","resolution":{"observed_at":"2026-08-10T22:03:56.877656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.03152","last_updated":"2021-04-28T04:44:18Z","snapshot_observed_at":"2026-07-06T10:57:17.838513Z","submitted_at":"2021-04-07T14:32:38Z","title":"TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.03152","snapshot_observed_at":"2026-08-10T22:03:56.848663Z","title":"ArXiv abs/2104.03152","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.848663Z"},"links":{"cited_paper":"/paper/2104.03152","citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:69b67f316c3737db167209dc8128864ab39a3a062b75565e25c01afec4a2dd00","observation_id":"463412b2-7b74-4342-9532-a0420a529075","resolution":{"observed_at":"2026-08-10T22:03:56.848663Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:56.845743Z","title":"2022 IEEE 18th International Conference on e-Science (e-Science) , 433–434doi:10.1109/eScience55777.2022.00074","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.845743Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:895e3ac1500e0a0d024637af315ffe62c26384cdeb780a0cdfda6264c99f589e","observation_id":"6d7e78e2-d79f-42df-a504-5b6f79f5e9e0","resolution":{"observed_at":"2026-08-10T22:03:56.845743Z","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":"10.31590/ejosat.1265586","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:03:57.015341Z","title":"European Journal of Science and Technology doi:10.31590/ejosat.1265586","venue":null,"work_id":"806127e5-3251-4489-9860-2ae460c80f17","year":null},"citing_paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-10T22:03:56.842409Z"},"links":{"citing_paper":"/paper/2501.02981"},"observation_digest":"sha256:cd2fb6985acea601b0b8f7cfdd7aa9e8403fbaf01fef5ab94b6a69b531100853","observation_id":"ed89ebed-28a3-4f26-9ab6-9dfcfbb584e2","resolution":{"observed_at":"2026-08-10T22:03:57.018681Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.02981","last_updated":"2025-01-07T08:39:10Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-11T00:19:05.338707Z","submitted_at":"2025-01-06T12:43:59Z","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":32,"verified_exact":6,"verified_fuzzy":3},"total_outbound_references":45},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.02981."}