{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:35DDEVNYII4I24CJXWC4XUHRFG","short_pith_number":"pith:35DDEVNY","schema_version":"1.0","canonical_sha256":"df463255b842388d7049bd85cbd0f129ade26793992986e97e41f8243ef32461","source":{"kind":"arxiv","id":"2007.02861","version":1},"attestation_state":"computed","paper":{"title":"Learning the Markov order of paths in a network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ingo Scholtes, Luka V. Petrovi\\'c","submitted_at":"2020-07-06T16:27:02Z","abstract_excerpt":"We study the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e. sequences of variable lengths where transitions between states are constrained to a known graph. Such data pose challenges for standard Markov order detection methods and demand modelling techniques that explicitly account for the graph constraint. Adopting a multi-order modelling framework for paths, we develop a Bayesian learning technique that (i) more reliably detects the correct Markov order compared to a competing method based on the likelihood ratio test, (ii) requires cons"},"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":"2007.02861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-06T16:27:02Z","cross_cats_sorted":["cs.SI","stat.ME","stat.ML"],"title_canon_sha256":"12c84031c88fb7020720e47d09984d370cebf3a56d18bde4b9f768dc6b16d476","abstract_canon_sha256":"e2efd83d0c0e2b95289507715cc6fcd3d15fe6d7783370f59cedf2893f32ed69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:16:35.545971Z","signature_b64":"Iqo+tH2g0sLAvbnmSBMAg+WBxvENi5aFe41HMIjqdZBes2P4k0qTe69hBHAYvsPbT/DjFTW7HRDBMiJndL2BBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df463255b842388d7049bd85cbd0f129ade26793992986e97e41f8243ef32461","last_reissued_at":"2026-07-05T01:16:35.545384Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:16:35.545384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning the Markov order of paths in a network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ingo Scholtes, Luka V. Petrovi\\'c","submitted_at":"2020-07-06T16:27:02Z","abstract_excerpt":"We study the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e. sequences of variable lengths where transitions between states are constrained to a known graph. Such data pose challenges for standard Markov order detection methods and demand modelling techniques that explicitly account for the graph constraint. Adopting a multi-order modelling framework for paths, we develop a Bayesian learning technique that (i) more reliably detects the correct Markov order compared to a competing method based on the likelihood ratio test, (ii) requires cons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.02861","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2007.02861/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2007.02861","created_at":"2026-07-05T01:16:35.545444+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.02861v1","created_at":"2026-07-05T01:16:35.545444+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.02861","created_at":"2026-07-05T01:16:35.545444+00:00"},{"alias_kind":"pith_short_12","alias_value":"35DDEVNYII4I","created_at":"2026-07-05T01:16:35.545444+00:00"},{"alias_kind":"pith_short_16","alias_value":"35DDEVNYII4I24CJ","created_at":"2026-07-05T01:16:35.545444+00:00"},{"alias_kind":"pith_short_8","alias_value":"35DDEVNY","created_at":"2026-07-05T01:16:35.545444+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.05976","citing_title":"HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG","json":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG.json","graph_json":"https://pith.science/api/pith-number/35DDEVNYII4I24CJXWC4XUHRFG/graph.json","events_json":"https://pith.science/api/pith-number/35DDEVNYII4I24CJXWC4XUHRFG/events.json","paper":"https://pith.science/paper/35DDEVNY"},"agent_actions":{"view_html":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG","download_json":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG.json","view_paper":"https://pith.science/paper/35DDEVNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.02861&json=true","fetch_graph":"https://pith.science/api/pith-number/35DDEVNYII4I24CJXWC4XUHRFG/graph.json","fetch_events":"https://pith.science/api/pith-number/35DDEVNYII4I24CJXWC4XUHRFG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG/action/storage_attestation","attest_author":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG/action/author_attestation","sign_citation":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG/action/citation_signature","submit_replication":"https://pith.science/pith/35DDEVNYII4I24CJXWC4XUHRFG/action/replication_record"}},"created_at":"2026-07-05T01:16:35.545444+00:00","updated_at":"2026-07-05T01:16:35.545444+00:00"}