{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AFQ5NS2LY2I6USD277IMIA2ZNC","short_pith_number":"pith:AFQ5NS2L","schema_version":"1.0","canonical_sha256":"0161d6cb4bc691ea487affd0c4035968a6154394656a7ce99bd542d6a3060334","source":{"kind":"arxiv","id":"1805.12421","version":6},"attestation_state":"computed","paper":{"title":"HOPF: Higher Order Propagation Framework for Deep Collective Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Balaraman Ravindran, Mitesh M. Khapra, Priyesh Vijayan, Srinivasan Parthasarathy, Yash Chandak","submitted_at":"2018-05-31T11:28:10Z","abstract_excerpt":"Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors. It is often the case that a node is not only influenced by its immediate neighbors but also by higher order neighbors, multiple hops away. Recent state-of-the-art models for CC learn end-to-end differentiable variations of Weisfeiler-Lehman (WL) kernels to aggregate multi-hop neighborhood information. In this work, we propose a Hig"},"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":"1805.12421","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-31T11:28:10Z","cross_cats_sorted":["cs.SI","stat.ML"],"title_canon_sha256":"f54c3863a66de979cf98d3045f21e910e02257f9128f1865ecdbdb151609bcb9","abstract_canon_sha256":"bb7c1ee13caeede5be0d108d56f6b4800b1d7a264519d6f3282e096964803c3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:55.439570Z","signature_b64":"FQGQXtyjCJrEameEnoe5SpIN7H/zRPIfpqiecv8qre/Tun2lpUJNYyaXoz81TFQI19eCiO71oOtbIifkyfo4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0161d6cb4bc691ea487affd0c4035968a6154394656a7ce99bd542d6a3060334","last_reissued_at":"2026-05-18T00:00:55.439047Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:55.439047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HOPF: Higher Order Propagation Framework for Deep Collective Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Balaraman Ravindran, Mitesh M. Khapra, Priyesh Vijayan, Srinivasan Parthasarathy, Yash Chandak","submitted_at":"2018-05-31T11:28:10Z","abstract_excerpt":"Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors. It is often the case that a node is not only influenced by its immediate neighbors but also by higher order neighbors, multiple hops away. Recent state-of-the-art models for CC learn end-to-end differentiable variations of Weisfeiler-Lehman (WL) kernels to aggregate multi-hop neighborhood information. In this work, we propose a Hig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.12421","kind":"arxiv","version":6},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1805.12421","created_at":"2026-05-18T00:00:55.439120+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.12421v6","created_at":"2026-05-18T00:00:55.439120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.12421","created_at":"2026-05-18T00:00:55.439120+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFQ5NS2LY2I6","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFQ5NS2LY2I6USD2","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFQ5NS2L","created_at":"2026-05-18T12:32:13.499390+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/AFQ5NS2LY2I6USD277IMIA2ZNC","json":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC.json","graph_json":"https://pith.science/api/pith-number/AFQ5NS2LY2I6USD277IMIA2ZNC/graph.json","events_json":"https://pith.science/api/pith-number/AFQ5NS2LY2I6USD277IMIA2ZNC/events.json","paper":"https://pith.science/paper/AFQ5NS2L"},"agent_actions":{"view_html":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC","download_json":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC.json","view_paper":"https://pith.science/paper/AFQ5NS2L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.12421&json=true","fetch_graph":"https://pith.science/api/pith-number/AFQ5NS2LY2I6USD277IMIA2ZNC/graph.json","fetch_events":"https://pith.science/api/pith-number/AFQ5NS2LY2I6USD277IMIA2ZNC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC/action/storage_attestation","attest_author":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC/action/author_attestation","sign_citation":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC/action/citation_signature","submit_replication":"https://pith.science/pith/AFQ5NS2LY2I6USD277IMIA2ZNC/action/replication_record"}},"created_at":"2026-05-18T00:00:55.439120+00:00","updated_at":"2026-05-18T00:00:55.439120+00:00"}