{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AR2UAYQR7SLGYX5VPCVYBGJT4L","short_pith_number":"pith:AR2UAYQR","schema_version":"1.0","canonical_sha256":"0475406211fc966c5fb578ab809933e2d7062b2b3434bb71ff54d7d429626fb0","source":{"kind":"arxiv","id":"2211.06962","version":1},"attestation_state":"computed","paper":{"title":"A Scalable Graph Neural Network Decoder for Short Block Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.IT"],"primary_cat":"cs.IT","authors_text":"Branka Vucetic, Changyang She, Chentao Yue, Kou Tian, Yonghui Li","submitted_at":"2022-11-13T17:13:12Z","abstract_excerpt":"In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure, which algorithmically aligns with the conventional belief propagation (BP) decoding method. In each iteration, the \"weight\" on the message passed along each edge is obtained from a fully connected neural network that has the reliability information from nodes/edges as its input. Compared to existing deep-learning-based decoding schemes, the EW-GNN decoder is characterised b"},"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":"2211.06962","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-11-13T17:13:12Z","cross_cats_sorted":["cs.AI","math.IT"],"title_canon_sha256":"db6aebfa59aa64d41821ac299e46a57e3fc027f9467106dffa87bb8f4ade9ad0","abstract_canon_sha256":"18815c0efd2be769d665f4a4b6356dc2f4bcbb5850c038a6b8677088453f648a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:47.913350Z","signature_b64":"+G7hnXVWjedy29ufxBPGw8ie0K/j7gxPOcoN1e4bvlETyUb31bj85NReqvI1tMbhGN1po1dTuHFIWLfAACI8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0475406211fc966c5fb578ab809933e2d7062b2b3434bb71ff54d7d429626fb0","last_reissued_at":"2026-07-05T05:15:47.912931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:47.912931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Scalable Graph Neural Network Decoder for Short Block Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.IT"],"primary_cat":"cs.IT","authors_text":"Branka Vucetic, Changyang She, Chentao Yue, Kou Tian, Yonghui Li","submitted_at":"2022-11-13T17:13:12Z","abstract_excerpt":"In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure, which algorithmically aligns with the conventional belief propagation (BP) decoding method. In each iteration, the \"weight\" on the message passed along each edge is obtained from a fully connected neural network that has the reliability information from nodes/edges as its input. Compared to existing deep-learning-based decoding schemes, the EW-GNN decoder is characterised b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.06962","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/2211.06962/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":"2211.06962","created_at":"2026-07-05T05:15:47.912980+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.06962v1","created_at":"2026-07-05T05:15:47.912980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.06962","created_at":"2026-07-05T05:15:47.912980+00:00"},{"alias_kind":"pith_short_12","alias_value":"AR2UAYQR7SLG","created_at":"2026-07-05T05:15:47.912980+00:00"},{"alias_kind":"pith_short_16","alias_value":"AR2UAYQR7SLGYX5V","created_at":"2026-07-05T05:15:47.912980+00:00"},{"alias_kind":"pith_short_8","alias_value":"AR2UAYQR","created_at":"2026-07-05T05:15:47.912980+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/AR2UAYQR7SLGYX5VPCVYBGJT4L","json":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L.json","graph_json":"https://pith.science/api/pith-number/AR2UAYQR7SLGYX5VPCVYBGJT4L/graph.json","events_json":"https://pith.science/api/pith-number/AR2UAYQR7SLGYX5VPCVYBGJT4L/events.json","paper":"https://pith.science/paper/AR2UAYQR"},"agent_actions":{"view_html":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L","download_json":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L.json","view_paper":"https://pith.science/paper/AR2UAYQR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.06962&json=true","fetch_graph":"https://pith.science/api/pith-number/AR2UAYQR7SLGYX5VPCVYBGJT4L/graph.json","fetch_events":"https://pith.science/api/pith-number/AR2UAYQR7SLGYX5VPCVYBGJT4L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L/action/storage_attestation","attest_author":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L/action/author_attestation","sign_citation":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L/action/citation_signature","submit_replication":"https://pith.science/pith/AR2UAYQR7SLGYX5VPCVYBGJT4L/action/replication_record"}},"created_at":"2026-07-05T05:15:47.912980+00:00","updated_at":"2026-07-05T05:15:47.912980+00:00"}