{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:GISPSGUSQRUC35ONE7K5PLYEW4","short_pith_number":"pith:GISPSGUS","schema_version":"1.0","canonical_sha256":"3224f91a9284682df5cd27d5d7af04b72876f20113b23678f25ea71b8b117456","source":{"kind":"arxiv","id":"2110.07190","version":1},"attestation_state":"computed","paper":{"title":"Why Propagate Alone? Parallel Use of Labels and Features on Graphs","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Wipf, Jiarui Jin, Jiuhai Chen, Quan Gan, Weinan Zhang, Yangkun Wang, Yongyi Yang, Yong Yu, Zengfeng Huang, Zheng Zhang","submitted_at":"2021-10-14T07:34:11Z","abstract_excerpt":"Graph neural networks (GNNs) and label propagation represent two interrelated modeling strategies designed to exploit graph structure in tasks such as node property prediction. The former is typically based on stacked message-passing layers that share neighborhood information to transform node features into predictive embeddings. In contrast, the latter involves spreading label information to unlabeled nodes via a parameter-free diffusion process, but operates independently of the node features. Given then that the material difference is merely whether features or labels are smoothed across 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":"2110.07190","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-14T07:34:11Z","cross_cats_sorted":[],"title_canon_sha256":"130d353af53258e5430778f36975bff80884d8eaf3fa0e6e6cb029285d94e59f","abstract_canon_sha256":"d5097f13974e42fec9bb8e971a92d4ddb36aff88922193eb37c7ffb9a65710d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:22:39.824973Z","signature_b64":"ZCsFGyppKfkHFUBuSufNmU2o6E0z+rVvlSZtCPPq7plxvHXpxFGzOnmmncH8R3RdfROjkw86yKd27GthoQUVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3224f91a9284682df5cd27d5d7af04b72876f20113b23678f25ea71b8b117456","last_reissued_at":"2026-07-05T03:22:39.824491Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:22:39.824491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Why Propagate Alone? Parallel Use of Labels and Features on Graphs","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Wipf, Jiarui Jin, Jiuhai Chen, Quan Gan, Weinan Zhang, Yangkun Wang, Yongyi Yang, Yong Yu, Zengfeng Huang, Zheng Zhang","submitted_at":"2021-10-14T07:34:11Z","abstract_excerpt":"Graph neural networks (GNNs) and label propagation represent two interrelated modeling strategies designed to exploit graph structure in tasks such as node property prediction. The former is typically based on stacked message-passing layers that share neighborhood information to transform node features into predictive embeddings. In contrast, the latter involves spreading label information to unlabeled nodes via a parameter-free diffusion process, but operates independently of the node features. Given then that the material difference is merely whether features or labels are smoothed across th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.07190","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/2110.07190/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":"2110.07190","created_at":"2026-07-05T03:22:39.824546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.07190v1","created_at":"2026-07-05T03:22:39.824546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.07190","created_at":"2026-07-05T03:22:39.824546+00:00"},{"alias_kind":"pith_short_12","alias_value":"GISPSGUSQRUC","created_at":"2026-07-05T03:22:39.824546+00:00"},{"alias_kind":"pith_short_16","alias_value":"GISPSGUSQRUC35ON","created_at":"2026-07-05T03:22:39.824546+00:00"},{"alias_kind":"pith_short_8","alias_value":"GISPSGUS","created_at":"2026-07-05T03:22:39.824546+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/GISPSGUSQRUC35ONE7K5PLYEW4","json":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4.json","graph_json":"https://pith.science/api/pith-number/GISPSGUSQRUC35ONE7K5PLYEW4/graph.json","events_json":"https://pith.science/api/pith-number/GISPSGUSQRUC35ONE7K5PLYEW4/events.json","paper":"https://pith.science/paper/GISPSGUS"},"agent_actions":{"view_html":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4","download_json":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4.json","view_paper":"https://pith.science/paper/GISPSGUS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.07190&json=true","fetch_graph":"https://pith.science/api/pith-number/GISPSGUSQRUC35ONE7K5PLYEW4/graph.json","fetch_events":"https://pith.science/api/pith-number/GISPSGUSQRUC35ONE7K5PLYEW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4/action/storage_attestation","attest_author":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4/action/author_attestation","sign_citation":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4/action/citation_signature","submit_replication":"https://pith.science/pith/GISPSGUSQRUC35ONE7K5PLYEW4/action/replication_record"}},"created_at":"2026-07-05T03:22:39.824546+00:00","updated_at":"2026-07-05T03:22:39.824546+00:00"}