{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3IFZACV7KE4DV57F2ELLQEGZY2","short_pith_number":"pith:3IFZACV7","schema_version":"1.0","canonical_sha256":"da0b900abf51383af7e5d116b810d9c6be3ce0de95c2942ccab5f63e545fbc9c","source":{"kind":"arxiv","id":"1906.04582","version":4},"attestation_state":"computed","paper":{"title":"Intertemporal Community Detection in Human Mobility Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.AP"],"primary_cat":"physics.soc-ph","authors_text":"Joseph Glasser, Mark He, Nikhil Kaza, Shankar Bhamidi","submitted_at":"2019-06-10T17:05:33Z","abstract_excerpt":"We introduce a community detection method that finds clusters in network time-series by introducing an algorithm that finds significantly interconnected nodes across time. These connections are either increasing, decreasing, or constant over time. Significance of nodal connectivity within a set is judged using the Weighted Configuration Null Model at each time-point, then a novel significance-testing scheme is used to assess connectivity at all time points and the direction of its time-trend. We apply this method to bikeshare networks in New York City and Chicago and taxicab pickups and dropof"},"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":"1906.04582","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.soc-ph","submitted_at":"2019-06-10T17:05:33Z","cross_cats_sorted":["cs.SI","stat.AP"],"title_canon_sha256":"2e059d90c8b748362dd32168816dfedb0728c61f6ad873034f61f846230e46ce","abstract_canon_sha256":"7ea71dd5b943acae7db5049088003a70c0cba6c77c1e8b72184d716fc42c9dce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:52:35.980700Z","signature_b64":"+rzzCxTakAS6X3T3F1lnqze7jfuPRTaAaCXTL5/ZvqN7XqwfswW94LpAq/3Yco+yNe0wIt25T6jeGF2dx+HhBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da0b900abf51383af7e5d116b810d9c6be3ce0de95c2942ccab5f63e545fbc9c","last_reissued_at":"2026-07-05T00:52:35.980260Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:52:35.980260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Intertemporal Community Detection in Human Mobility Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.AP"],"primary_cat":"physics.soc-ph","authors_text":"Joseph Glasser, Mark He, Nikhil Kaza, Shankar Bhamidi","submitted_at":"2019-06-10T17:05:33Z","abstract_excerpt":"We introduce a community detection method that finds clusters in network time-series by introducing an algorithm that finds significantly interconnected nodes across time. These connections are either increasing, decreasing, or constant over time. Significance of nodal connectivity within a set is judged using the Weighted Configuration Null Model at each time-point, then a novel significance-testing scheme is used to assess connectivity at all time points and the direction of its time-trend. We apply this method to bikeshare networks in New York City and Chicago and taxicab pickups and dropof"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04582","kind":"arxiv","version":4},"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/1906.04582/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":"1906.04582","created_at":"2026-07-05T00:52:35.980325+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04582v4","created_at":"2026-07-05T00:52:35.980325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04582","created_at":"2026-07-05T00:52:35.980325+00:00"},{"alias_kind":"pith_short_12","alias_value":"3IFZACV7KE4D","created_at":"2026-07-05T00:52:35.980325+00:00"},{"alias_kind":"pith_short_16","alias_value":"3IFZACV7KE4DV57F","created_at":"2026-07-05T00:52:35.980325+00:00"},{"alias_kind":"pith_short_8","alias_value":"3IFZACV7","created_at":"2026-07-05T00:52:35.980325+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.09440","citing_title":"Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2","json":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2.json","graph_json":"https://pith.science/api/pith-number/3IFZACV7KE4DV57F2ELLQEGZY2/graph.json","events_json":"https://pith.science/api/pith-number/3IFZACV7KE4DV57F2ELLQEGZY2/events.json","paper":"https://pith.science/paper/3IFZACV7"},"agent_actions":{"view_html":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2","download_json":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2.json","view_paper":"https://pith.science/paper/3IFZACV7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04582&json=true","fetch_graph":"https://pith.science/api/pith-number/3IFZACV7KE4DV57F2ELLQEGZY2/graph.json","fetch_events":"https://pith.science/api/pith-number/3IFZACV7KE4DV57F2ELLQEGZY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2/action/storage_attestation","attest_author":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2/action/author_attestation","sign_citation":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2/action/citation_signature","submit_replication":"https://pith.science/pith/3IFZACV7KE4DV57F2ELLQEGZY2/action/replication_record"}},"created_at":"2026-07-05T00:52:35.980325+00:00","updated_at":"2026-07-05T00:52:35.980325+00:00"}