{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DAJLGEYC6HB3O2Z3WZIQQRTZG7","short_pith_number":"pith:DAJLGEYC","schema_version":"1.0","canonical_sha256":"1812b31302f1c3b76b3bb65108467937d12ad4d3a6e3d73826a2eb2315d9f663","source":{"kind":"arxiv","id":"1908.01999","version":2},"attestation_state":"computed","paper":{"title":"Memory effects on link formation in temporal networks: A fractional calculus approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.soc-ph","authors_text":"F. Abbasi, F. Rabbani, G.R. Jafari, T. Khraisha","submitted_at":"2019-08-06T07:53:50Z","abstract_excerpt":"Memory plays a vital role in the temporal evolution of interactions of complex systems. To address the impact of memory on the temporal pattern of networks, we propose a simple preferential connection model, in which nodes have a preferential tendency to establish links with most active nodes. Node activity is measured by the number of links a node observes in a given time interval. Memory is investigated using a time-fractional order derivative equation, which has proven to be a powerful method to understand phenomena with long-term memory. The memoryless case reveals a characteristic time wh"},"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":"1908.01999","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.soc-ph","submitted_at":"2019-08-06T07:53:50Z","cross_cats_sorted":[],"title_canon_sha256":"550accd511e2297f06ee5bcf617617d9f3ee808921e6934ac1d3195411c42242","abstract_canon_sha256":"2ffb3aaad811cf568191f73e9ab6da70921d959ee5a7de54bc0fada8ef039a24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:35.260183Z","signature_b64":"GrLax6+Vc+o4b81tuExFQzkHQFNRSFSscTTD3W7OaCJ7qIa4Bfxlp9W2NjQHKnfEMHzYrn2rDh+oiwyB48iVCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1812b31302f1c3b76b3bb65108467937d12ad4d3a6e3d73826a2eb2315d9f663","last_reissued_at":"2026-07-05T00:33:35.259782Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:35.259782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Memory effects on link formation in temporal networks: A fractional calculus approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.soc-ph","authors_text":"F. Abbasi, F. Rabbani, G.R. Jafari, T. Khraisha","submitted_at":"2019-08-06T07:53:50Z","abstract_excerpt":"Memory plays a vital role in the temporal evolution of interactions of complex systems. To address the impact of memory on the temporal pattern of networks, we propose a simple preferential connection model, in which nodes have a preferential tendency to establish links with most active nodes. Node activity is measured by the number of links a node observes in a given time interval. Memory is investigated using a time-fractional order derivative equation, which has proven to be a powerful method to understand phenomena with long-term memory. The memoryless case reveals a characteristic time wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01999","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/1908.01999/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":"1908.01999","created_at":"2026-07-05T00:33:35.259841+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.01999v2","created_at":"2026-07-05T00:33:35.259841+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01999","created_at":"2026-07-05T00:33:35.259841+00:00"},{"alias_kind":"pith_short_12","alias_value":"DAJLGEYC6HB3","created_at":"2026-07-05T00:33:35.259841+00:00"},{"alias_kind":"pith_short_16","alias_value":"DAJLGEYC6HB3O2Z3","created_at":"2026-07-05T00:33:35.259841+00:00"},{"alias_kind":"pith_short_8","alias_value":"DAJLGEYC","created_at":"2026-07-05T00:33:35.259841+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.11433","citing_title":"Growth Dynamics of Value and Cost Trade-off in Temporal Networks","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7","json":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7.json","graph_json":"https://pith.science/api/pith-number/DAJLGEYC6HB3O2Z3WZIQQRTZG7/graph.json","events_json":"https://pith.science/api/pith-number/DAJLGEYC6HB3O2Z3WZIQQRTZG7/events.json","paper":"https://pith.science/paper/DAJLGEYC"},"agent_actions":{"view_html":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7","download_json":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7.json","view_paper":"https://pith.science/paper/DAJLGEYC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.01999&json=true","fetch_graph":"https://pith.science/api/pith-number/DAJLGEYC6HB3O2Z3WZIQQRTZG7/graph.json","fetch_events":"https://pith.science/api/pith-number/DAJLGEYC6HB3O2Z3WZIQQRTZG7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7/action/storage_attestation","attest_author":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7/action/author_attestation","sign_citation":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7/action/citation_signature","submit_replication":"https://pith.science/pith/DAJLGEYC6HB3O2Z3WZIQQRTZG7/action/replication_record"}},"created_at":"2026-07-05T00:33:35.259841+00:00","updated_at":"2026-07-05T00:33:35.259841+00:00"}