{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SFGPRISC4IKE4T6SAFSW7UT7YB","short_pith_number":"pith:SFGPRISC","schema_version":"1.0","canonical_sha256":"914cf8a242e2144e4fd201656fd27fc0503c1ce1412905ba79b2281e272af6ea","source":{"kind":"arxiv","id":"2402.09272","version":2},"attestation_state":"computed","paper":{"title":"Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Ben Steer, Cheick Tidiane Ba, Felix Cuadrado, Naomi A. Arnold, Peijie Zhong, Raul Mondragon, Renaud Lambiotte, Richard G. Clegg","submitted_at":"2024-02-14T15:59:24Z","abstract_excerpt":"Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoi"},"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":"2402.09272","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2024-02-14T15:59:24Z","cross_cats_sorted":[],"title_canon_sha256":"1ab2190e9c093c8576a3571577b7472e6e3f9a15f6d377e2363ab91011f44f67","abstract_canon_sha256":"c6a28b7096a6ca5a28b81de3a32a1763968623ef20530ae2716382f35ebf4f81"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:54.586737Z","signature_b64":"+TpTCI53VgabjNwJKC4tYyFq9Jw0KPduwVlH6ZyQNGkjqbEDOpKKOSB35EKIWfXrT+eJKx2PXrsZA1ebnF53CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"914cf8a242e2144e4fd201656fd27fc0503c1ce1412905ba79b2281e272af6ea","last_reissued_at":"2026-07-05T09:15:54.586190Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:54.586190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Ben Steer, Cheick Tidiane Ba, Felix Cuadrado, Naomi A. Arnold, Peijie Zhong, Raul Mondragon, Renaud Lambiotte, Richard G. Clegg","submitted_at":"2024-02-14T15:59:24Z","abstract_excerpt":"Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.09272","kind":"arxiv","version":2},"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/2402.09272/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":"2402.09272","created_at":"2026-07-05T09:15:54.586256+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.09272v2","created_at":"2026-07-05T09:15:54.586256+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.09272","created_at":"2026-07-05T09:15:54.586256+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFGPRISC4IKE","created_at":"2026-07-05T09:15:54.586256+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFGPRISC4IKE4T6S","created_at":"2026-07-05T09:15:54.586256+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFGPRISC","created_at":"2026-07-05T09:15:54.586256+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/SFGPRISC4IKE4T6SAFSW7UT7YB","json":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB.json","graph_json":"https://pith.science/api/pith-number/SFGPRISC4IKE4T6SAFSW7UT7YB/graph.json","events_json":"https://pith.science/api/pith-number/SFGPRISC4IKE4T6SAFSW7UT7YB/events.json","paper":"https://pith.science/paper/SFGPRISC"},"agent_actions":{"view_html":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB","download_json":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB.json","view_paper":"https://pith.science/paper/SFGPRISC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.09272&json=true","fetch_graph":"https://pith.science/api/pith-number/SFGPRISC4IKE4T6SAFSW7UT7YB/graph.json","fetch_events":"https://pith.science/api/pith-number/SFGPRISC4IKE4T6SAFSW7UT7YB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB/action/storage_attestation","attest_author":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB/action/author_attestation","sign_citation":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB/action/citation_signature","submit_replication":"https://pith.science/pith/SFGPRISC4IKE4T6SAFSW7UT7YB/action/replication_record"}},"created_at":"2026-07-05T09:15:54.586256+00:00","updated_at":"2026-07-05T09:15:54.586256+00:00"}