{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DHYS45MO62OJVKQIPDQ4JWRQQH","short_pith_number":"pith:DHYS45MO","schema_version":"1.0","canonical_sha256":"19f12e758ef69c9aaa0878e1c4da3081cd5835966cb0df7beae71404108c0e0b","source":{"kind":"arxiv","id":"2301.08210","version":1},"attestation_state":"computed","paper":{"title":"Everything is Connected: Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Petar Veli\\v{c}kovi\\'c","submitted_at":"2023-01-19T18:09:43Z","abstract_excerpt":"In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of th"},"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":"2301.08210","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-19T18:09:43Z","cross_cats_sorted":["cs.AI","cs.SI","stat.ML"],"title_canon_sha256":"5a653b3200e9c179968d6e077c1ce378e41864390494cfc3584aebf7095d7a62","abstract_canon_sha256":"fd898690b9aead1ff11c2baa7de2191cf97622e9cbb196bff3c82689f91a48bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:17.286362Z","signature_b64":"TUJWBTYD8iSTMu9xSSRjJaiLQWyxZjKudUV2rkxeFvjj59NqYGCM11NFhNPLQNq0VuGqcshnwL76OciBTQ3BDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19f12e758ef69c9aaa0878e1c4da3081cd5835966cb0df7beae71404108c0e0b","last_reissued_at":"2026-07-05T06:47:17.285877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:17.285877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Everything is Connected: Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Petar Veli\\v{c}kovi\\'c","submitted_at":"2023-01-19T18:09:43Z","abstract_excerpt":"In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.08210","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/2301.08210/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":"2301.08210","created_at":"2026-07-05T06:47:17.285931+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.08210v1","created_at":"2026-07-05T06:47:17.285931+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.08210","created_at":"2026-07-05T06:47:17.285931+00:00"},{"alias_kind":"pith_short_12","alias_value":"DHYS45MO62OJ","created_at":"2026-07-05T06:47:17.285931+00:00"},{"alias_kind":"pith_short_16","alias_value":"DHYS45MO62OJVKQI","created_at":"2026-07-05T06:47:17.285931+00:00"},{"alias_kind":"pith_short_8","alias_value":"DHYS45MO","created_at":"2026-07-05T06:47:17.285931+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.01045","citing_title":"Structured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH","json":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH.json","graph_json":"https://pith.science/api/pith-number/DHYS45MO62OJVKQIPDQ4JWRQQH/graph.json","events_json":"https://pith.science/api/pith-number/DHYS45MO62OJVKQIPDQ4JWRQQH/events.json","paper":"https://pith.science/paper/DHYS45MO"},"agent_actions":{"view_html":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH","download_json":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH.json","view_paper":"https://pith.science/paper/DHYS45MO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.08210&json=true","fetch_graph":"https://pith.science/api/pith-number/DHYS45MO62OJVKQIPDQ4JWRQQH/graph.json","fetch_events":"https://pith.science/api/pith-number/DHYS45MO62OJVKQIPDQ4JWRQQH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH/action/storage_attestation","attest_author":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH/action/author_attestation","sign_citation":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH/action/citation_signature","submit_replication":"https://pith.science/pith/DHYS45MO62OJVKQIPDQ4JWRQQH/action/replication_record"}},"created_at":"2026-07-05T06:47:17.285931+00:00","updated_at":"2026-07-05T06:47:17.285931+00:00"}