{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OFWIFFIC7EYI5D4X6ATVKAALJL","short_pith_number":"pith:OFWIFFIC","schema_version":"1.0","canonical_sha256":"716c829502f9308e8f97f02755000b4aee10de8fc652cb2ceda1d752dab43d0d","source":{"kind":"arxiv","id":"2010.00130","version":3},"attestation_state":"computed","paper":{"title":"Computing Graph Neural Networks: A Survey from Algorithms to Accelerators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Akshay Jain, Eduard Alarc\\'on, Jorge L\\'opez-Alonso, Robert Guirado, Sergi Abadal","submitted_at":"2020-09-30T22:29:27Z","abstract_excerpt":"Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication"},"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":"2010.00130","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-30T22:29:27Z","cross_cats_sorted":["cs.DC","stat.ML"],"title_canon_sha256":"c8ac4417f11ec21d4cf30b3aa55331ab80118a95ceedc08d5fb0798ec262e497","abstract_canon_sha256":"d4e99411ad8f3e3117c5e56d17cd32ca778743d6b90ef2487f30cc6f66ad8b81"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:07.074474Z","signature_b64":"JRvGCk6lTdGknSxsCeDuMYkRa4uuyNWlttjk1iMKndO5il1rDvzcvMJeKUt/f0y5j07igUbdWyt4kpiArfTcAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"716c829502f9308e8f97f02755000b4aee10de8fc652cb2ceda1d752dab43d0d","last_reissued_at":"2026-07-05T03:00:07.073990Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:07.073990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computing Graph Neural Networks: A Survey from Algorithms to Accelerators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Akshay Jain, Eduard Alarc\\'on, Jorge L\\'opez-Alonso, Robert Guirado, Sergi Abadal","submitted_at":"2020-09-30T22:29:27Z","abstract_excerpt":"Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.00130","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/2010.00130/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":"2010.00130","created_at":"2026-07-05T03:00:07.074056+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.00130v3","created_at":"2026-07-05T03:00:07.074056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.00130","created_at":"2026-07-05T03:00:07.074056+00:00"},{"alias_kind":"pith_short_12","alias_value":"OFWIFFIC7EYI","created_at":"2026-07-05T03:00:07.074056+00:00"},{"alias_kind":"pith_short_16","alias_value":"OFWIFFIC7EYI5D4X","created_at":"2026-07-05T03:00:07.074056+00:00"},{"alias_kind":"pith_short_8","alias_value":"OFWIFFIC","created_at":"2026-07-05T03:00:07.074056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11888","citing_title":"GNN Applied to Ego-nets for Friend Suggestions","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL","json":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL.json","graph_json":"https://pith.science/api/pith-number/OFWIFFIC7EYI5D4X6ATVKAALJL/graph.json","events_json":"https://pith.science/api/pith-number/OFWIFFIC7EYI5D4X6ATVKAALJL/events.json","paper":"https://pith.science/paper/OFWIFFIC"},"agent_actions":{"view_html":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL","download_json":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL.json","view_paper":"https://pith.science/paper/OFWIFFIC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.00130&json=true","fetch_graph":"https://pith.science/api/pith-number/OFWIFFIC7EYI5D4X6ATVKAALJL/graph.json","fetch_events":"https://pith.science/api/pith-number/OFWIFFIC7EYI5D4X6ATVKAALJL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL/action/storage_attestation","attest_author":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL/action/author_attestation","sign_citation":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL/action/citation_signature","submit_replication":"https://pith.science/pith/OFWIFFIC7EYI5D4X6ATVKAALJL/action/replication_record"}},"created_at":"2026-07-05T03:00:07.074056+00:00","updated_at":"2026-07-05T03:00:07.074056+00:00"}