{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HZC72AHO746WQ5US2NPDKTOS74","short_pith_number":"pith:HZC72AHO","schema_version":"1.0","canonical_sha256":"3e45fd00eeff3d687692d35e354dd2ff1b9abf40c2f08a442f854ab3149d128d","source":{"kind":"arxiv","id":"2210.07500","version":3},"attestation_state":"computed","paper":{"title":"ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Jianxiong Guo, Siwen Yan, Tiantian Chen, Weili Wu","submitted_at":"2022-10-14T03:56:53Z","abstract_excerpt":"Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee, time efficiency, generalization, etc. This makes it unable to adapt to large-scale networks and more complex applications. On the other side, with the latest achievements of Deep Reinforcement Learning (DRL) in artificial intelligence and "},"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":"2210.07500","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2022-10-14T03:56:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3707428046c31c02149ebd477f312297a40ba1363b18416196da370a27dc3bb4","abstract_canon_sha256":"eba0879d02f9acdac7fbc4f8173d9f462eeefd14145e5148e20b58543a56bf6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:10:20.487513Z","signature_b64":"KlqyQpd8hS/OwHUbI97l9udSzjv++oxKDHLV+J9RhLtYCzVNigVydSVdSP3CRzZf+AT6LrTBx/BFAunY2yWKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e45fd00eeff3d687692d35e354dd2ff1b9abf40c2f08a442f854ab3149d128d","last_reissued_at":"2026-07-05T06:10:20.487070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:10:20.487070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Jianxiong Guo, Siwen Yan, Tiantian Chen, Weili Wu","submitted_at":"2022-10-14T03:56:53Z","abstract_excerpt":"Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee, time efficiency, generalization, etc. This makes it unable to adapt to large-scale networks and more complex applications. On the other side, with the latest achievements of Deep Reinforcement Learning (DRL) in artificial intelligence and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.07500","kind":"arxiv","version":3},"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/2210.07500/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":"2210.07500","created_at":"2026-07-05T06:10:20.487125+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.07500v3","created_at":"2026-07-05T06:10:20.487125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.07500","created_at":"2026-07-05T06:10:20.487125+00:00"},{"alias_kind":"pith_short_12","alias_value":"HZC72AHO746W","created_at":"2026-07-05T06:10:20.487125+00:00"},{"alias_kind":"pith_short_16","alias_value":"HZC72AHO746WQ5US","created_at":"2026-07-05T06:10:20.487125+00:00"},{"alias_kind":"pith_short_8","alias_value":"HZC72AHO","created_at":"2026-07-05T06:10:20.487125+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.00779","citing_title":"REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74","json":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74.json","graph_json":"https://pith.science/api/pith-number/HZC72AHO746WQ5US2NPDKTOS74/graph.json","events_json":"https://pith.science/api/pith-number/HZC72AHO746WQ5US2NPDKTOS74/events.json","paper":"https://pith.science/paper/HZC72AHO"},"agent_actions":{"view_html":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74","download_json":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74.json","view_paper":"https://pith.science/paper/HZC72AHO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.07500&json=true","fetch_graph":"https://pith.science/api/pith-number/HZC72AHO746WQ5US2NPDKTOS74/graph.json","fetch_events":"https://pith.science/api/pith-number/HZC72AHO746WQ5US2NPDKTOS74/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74/action/storage_attestation","attest_author":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74/action/author_attestation","sign_citation":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74/action/citation_signature","submit_replication":"https://pith.science/pith/HZC72AHO746WQ5US2NPDKTOS74/action/replication_record"}},"created_at":"2026-07-05T06:10:20.487125+00:00","updated_at":"2026-07-05T06:10:20.487125+00:00"}