{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:AKTMNOZE7TBXLFZUIKTHFAES2J","short_pith_number":"pith:AKTMNOZE","schema_version":"1.0","canonical_sha256":"02a6c6bb24fcc375973442a6728092d25c875fc07acabecbb5ad7a1cba2257b9","source":{"kind":"arxiv","id":"2003.00330","version":7},"attestation_state":"computed","paper":{"title":"Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.LO"],"primary_cat":"cs.AI","authors_text":"Artur Garcez, Luis C. Lamb, Marcelo Prates, Marco Gori, Moshe Vardi, Pedro Avelar","submitted_at":"2020-02-29T18:55:13Z","abstract_excerpt":"Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-"},"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":"2003.00330","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2020-02-29T18:55:13Z","cross_cats_sorted":["cs.CL","cs.LG","cs.LO"],"title_canon_sha256":"b3ab5e53955e2531f5b31b1df16bbd8e97bd75b2b6b095f457f7da04953e7f79","abstract_canon_sha256":"1dd908f5315287be1ba727e85a215c2f44880edcda727f45bddd5e2b97163e61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:32.839005Z","signature_b64":"66ov6h7jl8Ec1ETZIDd7Sz/wBnWhL6hATwVkG9ES9GOlgaNnAK0eHL2dUhlnN9mRWCKpXAoLieqPwdCthjPmDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02a6c6bb24fcc375973442a6728092d25c875fc07acabecbb5ad7a1cba2257b9","last_reissued_at":"2026-07-05T02:48:32.838538Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:32.838538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.LO"],"primary_cat":"cs.AI","authors_text":"Artur Garcez, Luis C. Lamb, Marcelo Prates, Marco Gori, Moshe Vardi, Pedro Avelar","submitted_at":"2020-02-29T18:55:13Z","abstract_excerpt":"Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.00330","kind":"arxiv","version":7},"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/2003.00330/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":"2003.00330","created_at":"2026-07-05T02:48:32.838596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.00330v7","created_at":"2026-07-05T02:48:32.838596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.00330","created_at":"2026-07-05T02:48:32.838596+00:00"},{"alias_kind":"pith_short_12","alias_value":"AKTMNOZE7TBX","created_at":"2026-07-05T02:48:32.838596+00:00"},{"alias_kind":"pith_short_16","alias_value":"AKTMNOZE7TBXLFZU","created_at":"2026-07-05T02:48:32.838596+00:00"},{"alias_kind":"pith_short_8","alias_value":"AKTMNOZE","created_at":"2026-07-05T02:48:32.838596+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22429","citing_title":"Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation","ref_index":218,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29556","citing_title":"Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book","ref_index":115,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16471","citing_title":"Semantic Channel Theory: Deductive Compression and Structural Fidelity for Multi-Agent Communication","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J","json":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J.json","graph_json":"https://pith.science/api/pith-number/AKTMNOZE7TBXLFZUIKTHFAES2J/graph.json","events_json":"https://pith.science/api/pith-number/AKTMNOZE7TBXLFZUIKTHFAES2J/events.json","paper":"https://pith.science/paper/AKTMNOZE"},"agent_actions":{"view_html":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J","download_json":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J.json","view_paper":"https://pith.science/paper/AKTMNOZE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.00330&json=true","fetch_graph":"https://pith.science/api/pith-number/AKTMNOZE7TBXLFZUIKTHFAES2J/graph.json","fetch_events":"https://pith.science/api/pith-number/AKTMNOZE7TBXLFZUIKTHFAES2J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J/action/storage_attestation","attest_author":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J/action/author_attestation","sign_citation":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J/action/citation_signature","submit_replication":"https://pith.science/pith/AKTMNOZE7TBXLFZUIKTHFAES2J/action/replication_record"}},"created_at":"2026-07-05T02:48:32.838596+00:00","updated_at":"2026-07-05T02:48:32.838596+00:00"}