{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:PXHXQL5MVYREARQ3YMY3HSE6FQ","short_pith_number":"pith:PXHXQL5M","canonical_record":{"source":{"id":"1701.01470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-05T20:34:57Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"5da75c99e310638f939174c1998e078d19b425afa2d9b5deed1e753c234a038c","abstract_canon_sha256":"85948cd079fef3251a4e62ebbc4d767616d94836dbb2a3e1cf0ea51531e360a4"},"schema_version":"1.0"},"canonical_sha256":"7dcf782facae2240461bc331b3c89e2c3a57245c49b6049f9a2fa186a3e70235","source":{"kind":"arxiv","id":"1701.01470","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.01470","created_at":"2026-05-18T00:53:16Z"},{"alias_kind":"arxiv_version","alias_value":"1701.01470v1","created_at":"2026-05-18T00:53:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.01470","created_at":"2026-05-18T00:53:16Z"},{"alias_kind":"pith_short_12","alias_value":"PXHXQL5MVYRE","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"PXHXQL5MVYREARQ3","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"PXHXQL5M","created_at":"2026-05-18T12:31:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:PXHXQL5MVYREARQ3YMY3HSE6FQ","target":"record","payload":{"canonical_record":{"source":{"id":"1701.01470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-05T20:34:57Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"5da75c99e310638f939174c1998e078d19b425afa2d9b5deed1e753c234a038c","abstract_canon_sha256":"85948cd079fef3251a4e62ebbc4d767616d94836dbb2a3e1cf0ea51531e360a4"},"schema_version":"1.0"},"canonical_sha256":"7dcf782facae2240461bc331b3c89e2c3a57245c49b6049f9a2fa186a3e70235","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:16.748852Z","signature_b64":"M9XOd28t4AS1hr/jyZ2T4q0ja5u5GmMGiRNkDmOEaSuTgH5NolJiLw+seg7iZZZFMMrbXTkLQShoE76USVX8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7dcf782facae2240461bc331b3c89e2c3a57245c49b6049f9a2fa186a3e70235","last_reissued_at":"2026-05-18T00:53:16.748385Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:16.748385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.01470","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:53:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1XLtD5dYtK05J4+6guHMtWBFrLfZxxQA4K3jgAyDt7aLIqeKnm7T2LhnxmcmPXIFfi2bC43grTru86SofavjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T04:15:22.249261Z"},"content_sha256":"5fc90a542d06b9cdff115ed423a69dbc99020fb8c1e23060efc932db19ede57b","schema_version":"1.0","event_id":"sha256:5fc90a542d06b9cdff115ed423a69dbc99020fb8c1e23060efc932db19ede57b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:PXHXQL5MVYREARQ3YMY3HSE6FQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Structure Learning from Unlabeled Data for Event Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"stat.ML","authors_text":"Daniel B. Neill, Sriram Somanchi","submitted_at":"2017-01-05T20:34:57Z","abstract_excerpt":"Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that can be used to accurately detect future events of that type. Motivated by new theoretical results on the consistency of constrained and unconstrained subset scans, we propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subse"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.01470","kind":"arxiv","version":1},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:53:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ISBQJHwYJdaK/tDeHmjAiDDzwHI3hM2a+YDuPUsYALGlqBpnRbmoZEhswYHxrDrMxthyvYcLFogEh2eUGcjxDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T04:15:22.249844Z"},"content_sha256":"3c4b89c8da242e0bb06e922a638bb991ddfbf82978a5b21639247dcac9f8e3ee","schema_version":"1.0","event_id":"sha256:3c4b89c8da242e0bb06e922a638bb991ddfbf82978a5b21639247dcac9f8e3ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ/bundle.json","state_url":"https://pith.science/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T04:15:22Z","links":{"resolver":"https://pith.science/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ","bundle":"https://pith.science/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ/bundle.json","state":"https://pith.science/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PXHXQL5MVYREARQ3YMY3HSE6FQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:PXHXQL5MVYREARQ3YMY3HSE6FQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"85948cd079fef3251a4e62ebbc4d767616d94836dbb2a3e1cf0ea51531e360a4","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-05T20:34:57Z","title_canon_sha256":"5da75c99e310638f939174c1998e078d19b425afa2d9b5deed1e753c234a038c"},"schema_version":"1.0","source":{"id":"1701.01470","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.01470","created_at":"2026-05-18T00:53:16Z"},{"alias_kind":"arxiv_version","alias_value":"1701.01470v1","created_at":"2026-05-18T00:53:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.01470","created_at":"2026-05-18T00:53:16Z"},{"alias_kind":"pith_short_12","alias_value":"PXHXQL5MVYRE","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"PXHXQL5MVYREARQ3","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"PXHXQL5M","created_at":"2026-05-18T12:31:37Z"}],"graph_snapshots":[{"event_id":"sha256:3c4b89c8da242e0bb06e922a638bb991ddfbf82978a5b21639247dcac9f8e3ee","target":"graph","created_at":"2026-05-18T00:53:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that can be used to accurately detect future events of that type. Motivated by new theoretical results on the consistency of constrained and unconstrained subset scans, we propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subse","authors_text":"Daniel B. Neill, Sriram Somanchi","cross_cats":["cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-05T20:34:57Z","title":"Graph Structure Learning from Unlabeled Data for Event Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.01470","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5fc90a542d06b9cdff115ed423a69dbc99020fb8c1e23060efc932db19ede57b","target":"record","created_at":"2026-05-18T00:53:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"85948cd079fef3251a4e62ebbc4d767616d94836dbb2a3e1cf0ea51531e360a4","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-01-05T20:34:57Z","title_canon_sha256":"5da75c99e310638f939174c1998e078d19b425afa2d9b5deed1e753c234a038c"},"schema_version":"1.0","source":{"id":"1701.01470","kind":"arxiv","version":1}},"canonical_sha256":"7dcf782facae2240461bc331b3c89e2c3a57245c49b6049f9a2fa186a3e70235","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7dcf782facae2240461bc331b3c89e2c3a57245c49b6049f9a2fa186a3e70235","first_computed_at":"2026-05-18T00:53:16.748385Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:53:16.748385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M9XOd28t4AS1hr/jyZ2T4q0ja5u5GmMGiRNkDmOEaSuTgH5NolJiLw+seg7iZZZFMMrbXTkLQShoE76USVX8BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:53:16.748852Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.01470","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fc90a542d06b9cdff115ed423a69dbc99020fb8c1e23060efc932db19ede57b","sha256:3c4b89c8da242e0bb06e922a638bb991ddfbf82978a5b21639247dcac9f8e3ee"],"state_sha256":"a5d96067954d4cf79689b17409ed4836005814385896e067d25ad073e4dc2e38"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7k8i+qZ4F+lWNOF7JFqjOZzgB0uCrV0bwQ6NRFpgv7aHTy4vWiQqE73cJakb5fCj5RJZwsZhLrcGT6R1qk7gCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T04:15:22.253059Z","bundle_sha256":"8cce4b117f48d961766040a0968f04350363ede90d3a3b867e9b0bbd51780b39"}}