{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LPEZYYHYEMAU5BOGXOA2AW75QE","short_pith_number":"pith:LPEZYYHY","schema_version":"1.0","canonical_sha256":"5bc99c60f823014e85c6bb81a05bfd811e2671e05bc8ea93c3dfd098f856a1e1","source":{"kind":"arxiv","id":"1708.07967","version":1},"attestation_state":"computed","paper":{"title":"Faster Clustering via Non-Backtracking Random Walks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"stat.ML","authors_text":"Anuththari Gamage, Brian Rappaport, Shuchin Aeron","submitted_at":"2017-08-26T13:40:22Z","abstract_excerpt":"This paper presents VEC-NBT, a variation on the unsupervised graph clustering technique VEC, which improves upon the performance of the original algorithm significantly for sparse graphs. VEC employs a novel application of the state-of-the-art word2vec model to embed a graph in Euclidean space via random walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm to use a non-backtracking random walk instead of the normal backtracking random walk used in VEC. We introduce a modification to a non-backtracking random walk, which we call a begrudgingly-backtracking random walk, "},"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":"1708.07967","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-26T13:40:22Z","cross_cats_sorted":["cs.LG","cs.SI"],"title_canon_sha256":"4383196cadc0cdd7ef2b551cd12628e3f317c757537c916f258d39b10c2fb64c","abstract_canon_sha256":"aab6d10c4aecc6f4170dea4ded8255b5dc6d40876cf44ff5fe105cd77686d435"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:37.487716Z","signature_b64":"NXAJGnfeuAIkswB1L7gaoigcprNEf3avgGbhAWOut+fK6PambUefDaAby/Qinjg761UHRsokUqw8vysG+xUnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bc99c60f823014e85c6bb81a05bfd811e2671e05bc8ea93c3dfd098f856a1e1","last_reissued_at":"2026-05-18T00:36:37.487005Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:37.487005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Faster Clustering via Non-Backtracking Random Walks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"stat.ML","authors_text":"Anuththari Gamage, Brian Rappaport, Shuchin Aeron","submitted_at":"2017-08-26T13:40:22Z","abstract_excerpt":"This paper presents VEC-NBT, a variation on the unsupervised graph clustering technique VEC, which improves upon the performance of the original algorithm significantly for sparse graphs. VEC employs a novel application of the state-of-the-art word2vec model to embed a graph in Euclidean space via random walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm to use a non-backtracking random walk instead of the normal backtracking random walk used in VEC. We introduce a modification to a non-backtracking random walk, which we call a begrudgingly-backtracking random walk, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.07967","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":""},"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":"1708.07967","created_at":"2026-05-18T00:36:37.487116+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.07967v1","created_at":"2026-05-18T00:36:37.487116+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.07967","created_at":"2026-05-18T00:36:37.487116+00:00"},{"alias_kind":"pith_short_12","alias_value":"LPEZYYHYEMAU","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LPEZYYHYEMAU5BOG","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LPEZYYHY","created_at":"2026-05-18T12:31:28.150371+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/LPEZYYHYEMAU5BOGXOA2AW75QE","json":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE.json","graph_json":"https://pith.science/api/pith-number/LPEZYYHYEMAU5BOGXOA2AW75QE/graph.json","events_json":"https://pith.science/api/pith-number/LPEZYYHYEMAU5BOGXOA2AW75QE/events.json","paper":"https://pith.science/paper/LPEZYYHY"},"agent_actions":{"view_html":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE","download_json":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE.json","view_paper":"https://pith.science/paper/LPEZYYHY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.07967&json=true","fetch_graph":"https://pith.science/api/pith-number/LPEZYYHYEMAU5BOGXOA2AW75QE/graph.json","fetch_events":"https://pith.science/api/pith-number/LPEZYYHYEMAU5BOGXOA2AW75QE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE/action/storage_attestation","attest_author":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE/action/author_attestation","sign_citation":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE/action/citation_signature","submit_replication":"https://pith.science/pith/LPEZYYHYEMAU5BOGXOA2AW75QE/action/replication_record"}},"created_at":"2026-05-18T00:36:37.487116+00:00","updated_at":"2026-05-18T00:36:37.487116+00:00"}