{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SFFPGDTBLEGQWXPZ7KCWMDV7U2","short_pith_number":"pith:SFFPGDTB","schema_version":"1.0","canonical_sha256":"914af30e61590d0b5df9fa85660ebfa68480dea22bd63d711d04b5a7dd25fda1","source":{"kind":"arxiv","id":"1606.01754","version":1},"attestation_state":"computed","paper":{"title":"A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DM","cs.SI","math.OC"],"primary_cat":"cs.DS","authors_text":"Aravind Rajeswaran, Shankar Narasimhan, Sridharakumar Narasimhan","submitted_at":"2016-06-03T12:30:26Z","abstract_excerpt":"Leak detection in urban water distribution networks (WDNs) is challenging given their scale, complexity, and limited instrumentation. We present an algorithm for leak detection in WDNs, which involves making additional flow measurements on-demand, and repeated use of water balance. Graph partitioning is used to determine the location of flow measurements, with the objective to minimize the measurement cost. We follow a multi-stage divide and conquer approach. In every stage, a section of the WDN identified to contain the leak is partitioned into two or more sub-networks, and water balance is u"},"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":"1606.01754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-06-03T12:30:26Z","cross_cats_sorted":["cs.DM","cs.SI","math.OC"],"title_canon_sha256":"3d9a77fe259355f31a097f4db9069bf063a8a43cb5faba8b5b9207dfe65169ae","abstract_canon_sha256":"23cf216826527ffbbb44aa50be42701cfc164a26f3aa8beb7616c3bdac77bb5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:53.641953Z","signature_b64":"wYEdZDF4b7ff0PeclFf2dkxMfzSVwvU//3XywikYXCw3Wxqs6JVjZO5R7m5Z1ubL8dFFmHt4QOi4IwSVe8mJAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"914af30e61590d0b5df9fa85660ebfa68480dea22bd63d711d04b5a7dd25fda1","last_reissued_at":"2026-05-18T01:12:53.641591Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:53.641591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DM","cs.SI","math.OC"],"primary_cat":"cs.DS","authors_text":"Aravind Rajeswaran, Shankar Narasimhan, Sridharakumar Narasimhan","submitted_at":"2016-06-03T12:30:26Z","abstract_excerpt":"Leak detection in urban water distribution networks (WDNs) is challenging given their scale, complexity, and limited instrumentation. We present an algorithm for leak detection in WDNs, which involves making additional flow measurements on-demand, and repeated use of water balance. Graph partitioning is used to determine the location of flow measurements, with the objective to minimize the measurement cost. We follow a multi-stage divide and conquer approach. In every stage, a section of the WDN identified to contain the leak is partitioned into two or more sub-networks, and water balance is u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.01754","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":"1606.01754","created_at":"2026-05-18T01:12:53.641650+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.01754v1","created_at":"2026-05-18T01:12:53.641650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.01754","created_at":"2026-05-18T01:12:53.641650+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFFPGDTBLEGQ","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFFPGDTBLEGQWXPZ","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFFPGDTB","created_at":"2026-05-18T12:30:44.179134+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/SFFPGDTBLEGQWXPZ7KCWMDV7U2","json":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2.json","graph_json":"https://pith.science/api/pith-number/SFFPGDTBLEGQWXPZ7KCWMDV7U2/graph.json","events_json":"https://pith.science/api/pith-number/SFFPGDTBLEGQWXPZ7KCWMDV7U2/events.json","paper":"https://pith.science/paper/SFFPGDTB"},"agent_actions":{"view_html":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2","download_json":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2.json","view_paper":"https://pith.science/paper/SFFPGDTB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.01754&json=true","fetch_graph":"https://pith.science/api/pith-number/SFFPGDTBLEGQWXPZ7KCWMDV7U2/graph.json","fetch_events":"https://pith.science/api/pith-number/SFFPGDTBLEGQWXPZ7KCWMDV7U2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2/action/storage_attestation","attest_author":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2/action/author_attestation","sign_citation":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2/action/citation_signature","submit_replication":"https://pith.science/pith/SFFPGDTBLEGQWXPZ7KCWMDV7U2/action/replication_record"}},"created_at":"2026-05-18T01:12:53.641650+00:00","updated_at":"2026-05-18T01:12:53.641650+00:00"}