{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:SYMYIRTAL2IG2SW7SF7BGDEUKX","short_pith_number":"pith:SYMYIRTA","schema_version":"1.0","canonical_sha256":"96198446605e906d4adf917e130c9455cf89bafdca0dfa783a02374aa4dd1628","source":{"kind":"arxiv","id":"1311.5552","version":3},"attestation_state":"computed","paper":{"title":"Bayesian Discovery of Threat Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","physics.soc-ph","stat.ML","stat.TH"],"primary_cat":"cs.SI","authors_text":"Edward K. Kao, Garrett Bernstein, Kenneth D. Senne, Scott Philips, Steven T. Smith","submitted_at":"2013-11-21T20:43:44Z","abstract_excerpt":"A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utiliz"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1311.5552","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2013-11-21T20:43:44Z","cross_cats_sorted":["cs.LG","math.ST","physics.soc-ph","stat.ML","stat.TH"],"title_canon_sha256":"b5a26b8690ba1197887f2691ccbfa8581bc77044de180f90de774053668319ed","abstract_canon_sha256":"5f98a1d76c04c528b45859c6da538288e9dc3007d5ace50f4f4200016e02bfb1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:24.233986Z","signature_b64":"bDO+bYxCa7kGWjkQpu7JVseQ2XX7TSod5vn7VcrAH9X5EDuCiLYdOER0sb5cQTbWlqhRi1UMyfowWPe2RTinCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96198446605e906d4adf917e130c9455cf89bafdca0dfa783a02374aa4dd1628","last_reissued_at":"2026-05-18T02:43:24.233471Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:24.233471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Discovery of Threat Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","physics.soc-ph","stat.ML","stat.TH"],"primary_cat":"cs.SI","authors_text":"Edward K. Kao, Garrett Bernstein, Kenneth D. Senne, Scott Philips, Steven T. Smith","submitted_at":"2013-11-21T20:43:44Z","abstract_excerpt":"A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utiliz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.5552","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1311.5552","created_at":"2026-05-18T02:43:24.233532+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.5552v3","created_at":"2026-05-18T02:43:24.233532+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.5552","created_at":"2026-05-18T02:43:24.233532+00:00"},{"alias_kind":"pith_short_12","alias_value":"SYMYIRTAL2IG","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_16","alias_value":"SYMYIRTAL2IG2SW7","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_8","alias_value":"SYMYIRTA","created_at":"2026-05-18T12:27:59.945178+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX","json":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX.json","graph_json":"https://pith.science/api/pith-number/SYMYIRTAL2IG2SW7SF7BGDEUKX/graph.json","events_json":"https://pith.science/api/pith-number/SYMYIRTAL2IG2SW7SF7BGDEUKX/events.json","paper":"https://pith.science/paper/SYMYIRTA"},"agent_actions":{"view_html":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX","download_json":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX.json","view_paper":"https://pith.science/paper/SYMYIRTA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.5552&json=true","fetch_graph":"https://pith.science/api/pith-number/SYMYIRTAL2IG2SW7SF7BGDEUKX/graph.json","fetch_events":"https://pith.science/api/pith-number/SYMYIRTAL2IG2SW7SF7BGDEUKX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX/action/storage_attestation","attest_author":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX/action/author_attestation","sign_citation":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX/action/citation_signature","submit_replication":"https://pith.science/pith/SYMYIRTAL2IG2SW7SF7BGDEUKX/action/replication_record"}},"created_at":"2026-05-18T02:43:24.233532+00:00","updated_at":"2026-05-18T02:43:24.233532+00:00"}