{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ABVGO4YFQTQMLNIZALU7ZCA4VO","short_pith_number":"pith:ABVGO4YF","schema_version":"1.0","canonical_sha256":"006a67730584e0c5b51902e9fc881cab80aa1a468fb6a96f3f09556fda25311c","source":{"kind":"arxiv","id":"2507.19702","version":1},"attestation_state":"computed","paper":{"title":"A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Abdulhakeem O. Mohammed, Mohammed A. Ramadhan","submitted_at":"2025-07-25T22:45:56Z","abstract_excerpt":"Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing approaches often face trade-offs between accuracy and computational efficiency. To address these challenges, we propose 1D-CGS, a lightweight and effective hybrid model that integrates the speed of one-dimensional convolutional neural networks (1D-CNN) with the topological representation power of GraphSAGE for efficient node ranking. The model uses a lightweight input representation built on two straightforward and significant topological features: "},"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":"2507.19702","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2025-07-25T22:45:56Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"442f432476166cfa3a79573ccf630841ac759c14dd044ee0480b79bbe7f281f1","abstract_canon_sha256":"50400b0d96c1f75a50de42839ee9c69c2b44f40b6f5119278ecf3b9ff0d59cec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:07.362616Z","signature_b64":"wAHWh6R5AVaZJsbttmsLrA5txXm4bnvqNo7cFLOa3BX/cBq+PP3ZaeCH7Jy0cKmCRFDdPKxrs3W6YC+apDBFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"006a67730584e0c5b51902e9fc881cab80aa1a468fb6a96f3f09556fda25311c","last_reissued_at":"2026-06-02T02:04:07.362012Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:07.362012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.SI","authors_text":"Abdulhakeem O. Mohammed, Mohammed A. Ramadhan","submitted_at":"2025-07-25T22:45:56Z","abstract_excerpt":"Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing approaches often face trade-offs between accuracy and computational efficiency. To address these challenges, we propose 1D-CGS, a lightweight and effective hybrid model that integrates the speed of one-dimensional convolutional neural networks (1D-CNN) with the topological representation power of GraphSAGE for efficient node ranking. The model uses a lightweight input representation built on two straightforward and significant topological features: "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.19702","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/2507.19702/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":"2507.19702","created_at":"2026-06-02T02:04:07.362086+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.19702v1","created_at":"2026-06-02T02:04:07.362086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.19702","created_at":"2026-06-02T02:04:07.362086+00:00"},{"alias_kind":"pith_short_12","alias_value":"ABVGO4YFQTQM","created_at":"2026-06-02T02:04:07.362086+00:00"},{"alias_kind":"pith_short_16","alias_value":"ABVGO4YFQTQMLNIZ","created_at":"2026-06-02T02:04:07.362086+00:00"},{"alias_kind":"pith_short_8","alias_value":"ABVGO4YF","created_at":"2026-06-02T02:04:07.362086+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/ABVGO4YFQTQMLNIZALU7ZCA4VO","json":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO.json","graph_json":"https://pith.science/api/pith-number/ABVGO4YFQTQMLNIZALU7ZCA4VO/graph.json","events_json":"https://pith.science/api/pith-number/ABVGO4YFQTQMLNIZALU7ZCA4VO/events.json","paper":"https://pith.science/paper/ABVGO4YF"},"agent_actions":{"view_html":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO","download_json":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO.json","view_paper":"https://pith.science/paper/ABVGO4YF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.19702&json=true","fetch_graph":"https://pith.science/api/pith-number/ABVGO4YFQTQMLNIZALU7ZCA4VO/graph.json","fetch_events":"https://pith.science/api/pith-number/ABVGO4YFQTQMLNIZALU7ZCA4VO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO/action/storage_attestation","attest_author":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO/action/author_attestation","sign_citation":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO/action/citation_signature","submit_replication":"https://pith.science/pith/ABVGO4YFQTQMLNIZALU7ZCA4VO/action/replication_record"}},"created_at":"2026-06-02T02:04:07.362086+00:00","updated_at":"2026-06-02T02:04:07.362086+00:00"}