{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:YEVRSFFWE24PZ7SCD3Y3VLHQWI","short_pith_number":"pith:YEVRSFFW","schema_version":"1.0","canonical_sha256":"c12b1914b626b8fcfe421ef1baacf0b2027e1aa9d617c777186a9aecc5568981","source":{"kind":"arxiv","id":"1606.06450","version":1},"attestation_state":"computed","paper":{"title":"Limited Random Walk Algorithm for Big Graph Data Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Honglei Zhang, Jenni Raitoharju, Moncef Gabbouj, Serkan Kiranyaz","submitted_at":"2016-06-21T07:19:59Z","abstract_excerpt":"Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackle"},"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":"1606.06450","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-06-21T07:19:59Z","cross_cats_sorted":["physics.soc-ph"],"title_canon_sha256":"9f12786ecd2379d6c20d37401666742715e27cc1e3041a9b76b45ad54f5137d9","abstract_canon_sha256":"8becdfb9dabb6bdaebae2cbfd2604a56b128dae180737087514cf44273e4c8c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:09.523937Z","signature_b64":"6DW9XU0t7KarStnSxTWL6n6jJKFT9wAV6kaZVrJn60R4vqy8xjVq7zazQvfyZN2WW5XOzC502ah93FEP2DjaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c12b1914b626b8fcfe421ef1baacf0b2027e1aa9d617c777186a9aecc5568981","last_reissued_at":"2026-05-18T01:12:09.523394Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:09.523394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Limited Random Walk Algorithm for Big Graph Data Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Honglei Zhang, Jenni Raitoharju, Moncef Gabbouj, Serkan Kiranyaz","submitted_at":"2016-06-21T07:19:59Z","abstract_excerpt":"Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackle"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.06450","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":"1606.06450","created_at":"2026-05-18T01:12:09.523476+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.06450v1","created_at":"2026-05-18T01:12:09.523476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.06450","created_at":"2026-05-18T01:12:09.523476+00:00"},{"alias_kind":"pith_short_12","alias_value":"YEVRSFFWE24P","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"YEVRSFFWE24PZ7SC","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"YEVRSFFW","created_at":"2026-05-18T12:30:53.716459+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/YEVRSFFWE24PZ7SCD3Y3VLHQWI","json":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI.json","graph_json":"https://pith.science/api/pith-number/YEVRSFFWE24PZ7SCD3Y3VLHQWI/graph.json","events_json":"https://pith.science/api/pith-number/YEVRSFFWE24PZ7SCD3Y3VLHQWI/events.json","paper":"https://pith.science/paper/YEVRSFFW"},"agent_actions":{"view_html":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI","download_json":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI.json","view_paper":"https://pith.science/paper/YEVRSFFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.06450&json=true","fetch_graph":"https://pith.science/api/pith-number/YEVRSFFWE24PZ7SCD3Y3VLHQWI/graph.json","fetch_events":"https://pith.science/api/pith-number/YEVRSFFWE24PZ7SCD3Y3VLHQWI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI/action/storage_attestation","attest_author":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI/action/author_attestation","sign_citation":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI/action/citation_signature","submit_replication":"https://pith.science/pith/YEVRSFFWE24PZ7SCD3Y3VLHQWI/action/replication_record"}},"created_at":"2026-05-18T01:12:09.523476+00:00","updated_at":"2026-05-18T01:12:09.523476+00:00"}