{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:W3TAJPV7QRQB2IKLLV2DJHYB7P","short_pith_number":"pith:W3TAJPV7","canonical_record":{"source":{"id":"1605.08490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-05-27T01:55:31Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"fc09f676ea7db22d686493ee8621de21d452a28f69710f579b1f2f861b24c590","abstract_canon_sha256":"e605c8ca24016fda4d62d1a07223b6a6a1cf67f717e4cd3d304519c930b9188f"},"schema_version":"1.0"},"canonical_sha256":"b6e604bebf84601d214b5d74349f01fbd7d3bc8d09d6093e3c97218c013fcfc5","source":{"kind":"arxiv","id":"1605.08490","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.08490","created_at":"2026-05-18T01:13:29Z"},{"alias_kind":"arxiv_version","alias_value":"1605.08490v1","created_at":"2026-05-18T01:13:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.08490","created_at":"2026-05-18T01:13:29Z"},{"alias_kind":"pith_short_12","alias_value":"W3TAJPV7QRQB","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"W3TAJPV7QRQB2IKL","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"W3TAJPV7","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:W3TAJPV7QRQB2IKLLV2DJHYB7P","target":"record","payload":{"canonical_record":{"source":{"id":"1605.08490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-05-27T01:55:31Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"fc09f676ea7db22d686493ee8621de21d452a28f69710f579b1f2f861b24c590","abstract_canon_sha256":"e605c8ca24016fda4d62d1a07223b6a6a1cf67f717e4cd3d304519c930b9188f"},"schema_version":"1.0"},"canonical_sha256":"b6e604bebf84601d214b5d74349f01fbd7d3bc8d09d6093e3c97218c013fcfc5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:29.791271Z","signature_b64":"vzffrndTwxuqGWbHbWNwJR2znaH4BfHh8TWHFlzSGs84YKMXwttoHlAO3vQjofAOG/ZUDMMKa+ncOiRP/FsFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6e604bebf84601d214b5d74349f01fbd7d3bc8d09d6093e3c97218c013fcfc5","last_reissued_at":"2026-05-18T01:13:29.790610Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:29.790610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1605.08490","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:13:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KvPSPsR2Hd/U/oloqpDbnpXBcQDZs0VOdt9qlyT4XUEUIcJ8BNSPUFTIIYovRwTbcO+C07yDnsQ5Non8L9FFDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T22:24:14.499424Z"},"content_sha256":"11c4a4213192b325daa2e3ca384684e5d6d7362bd998da92928dd4b31ac95efa","schema_version":"1.0","event_id":"sha256:11c4a4213192b325daa2e3ca384684e5d6d7362bd998da92928dd4b31ac95efa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:W3TAJPV7QRQB2IKLLV2DJHYB7P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Simple and Strongly-Local Flow-Based Method for Cut Improvement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.SI","authors_text":"David F. Gleich, Michael W. Mahoney, Nate Veldt","submitted_at":"2016-05-27T01:55:31Z","abstract_excerpt":"Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We introduce and analyze a new method for locally-biased graph-based learning called SimpleLocal, which finds good conductance cuts near a set of seed vertices. An important feature of our algorithm is that it is strongly-local, meaning it does not need to explore the entire graph to find cuts that are locally optimal. This method solves the same objective as existing strongly-local flow-based methods, but it enables a simp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.08490","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:13:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XB+pCl06dZD+CdUHxGmU22ANAfeOSKIcrXmsHjC/qS5vOGy00b6Pv3NE88R9Zkx7FVKJxkRujHwseUDooMsLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T22:24:14.499769Z"},"content_sha256":"4d44bd974af919eeb8b604cfa5dd1e659fb896c1ec0ab66ff99bcffc801c661b","schema_version":"1.0","event_id":"sha256:4d44bd974af919eeb8b604cfa5dd1e659fb896c1ec0ab66ff99bcffc801c661b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P/bundle.json","state_url":"https://pith.science/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-21T22:24:14Z","links":{"resolver":"https://pith.science/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P","bundle":"https://pith.science/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P/bundle.json","state":"https://pith.science/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W3TAJPV7QRQB2IKLLV2DJHYB7P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:W3TAJPV7QRQB2IKLLV2DJHYB7P","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e605c8ca24016fda4d62d1a07223b6a6a1cf67f717e4cd3d304519c930b9188f","cross_cats_sorted":["cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-05-27T01:55:31Z","title_canon_sha256":"fc09f676ea7db22d686493ee8621de21d452a28f69710f579b1f2f861b24c590"},"schema_version":"1.0","source":{"id":"1605.08490","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.08490","created_at":"2026-05-18T01:13:29Z"},{"alias_kind":"arxiv_version","alias_value":"1605.08490v1","created_at":"2026-05-18T01:13:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.08490","created_at":"2026-05-18T01:13:29Z"},{"alias_kind":"pith_short_12","alias_value":"W3TAJPV7QRQB","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"W3TAJPV7QRQB2IKL","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"W3TAJPV7","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:4d44bd974af919eeb8b604cfa5dd1e659fb896c1ec0ab66ff99bcffc801c661b","target":"graph","created_at":"2026-05-18T01:13:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We introduce and analyze a new method for locally-biased graph-based learning called SimpleLocal, which finds good conductance cuts near a set of seed vertices. An important feature of our algorithm is that it is strongly-local, meaning it does not need to explore the entire graph to find cuts that are locally optimal. This method solves the same objective as existing strongly-local flow-based methods, but it enables a simp","authors_text":"David F. Gleich, Michael W. Mahoney, Nate Veldt","cross_cats":["cs.DS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-05-27T01:55:31Z","title":"A Simple and Strongly-Local Flow-Based Method for Cut Improvement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.08490","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:11c4a4213192b325daa2e3ca384684e5d6d7362bd998da92928dd4b31ac95efa","target":"record","created_at":"2026-05-18T01:13:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e605c8ca24016fda4d62d1a07223b6a6a1cf67f717e4cd3d304519c930b9188f","cross_cats_sorted":["cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-05-27T01:55:31Z","title_canon_sha256":"fc09f676ea7db22d686493ee8621de21d452a28f69710f579b1f2f861b24c590"},"schema_version":"1.0","source":{"id":"1605.08490","kind":"arxiv","version":1}},"canonical_sha256":"b6e604bebf84601d214b5d74349f01fbd7d3bc8d09d6093e3c97218c013fcfc5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b6e604bebf84601d214b5d74349f01fbd7d3bc8d09d6093e3c97218c013fcfc5","first_computed_at":"2026-05-18T01:13:29.790610Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:13:29.790610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vzffrndTwxuqGWbHbWNwJR2znaH4BfHh8TWHFlzSGs84YKMXwttoHlAO3vQjofAOG/ZUDMMKa+ncOiRP/FsFCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:13:29.791271Z","signed_message":"canonical_sha256_bytes"},"source_id":"1605.08490","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11c4a4213192b325daa2e3ca384684e5d6d7362bd998da92928dd4b31ac95efa","sha256:4d44bd974af919eeb8b604cfa5dd1e659fb896c1ec0ab66ff99bcffc801c661b"],"state_sha256":"392a84c77b9d1cfa7f94a14335ed2b1b63ccccac6cf93725d5137b6f25d07014"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xql67XobNZuxUEMc9tdJtM70VNdw82tg7T2u9fwpEbs70+pjp5mlAsBA7t44t+Lm9/c+20IIMOo0DwrZ97CXDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T22:24:14.501604Z","bundle_sha256":"03f0a297f00623aafd7c1d1959edc3eafe8f68d02c3ab199ffb731066bbdaa6b"}}