{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:QPGUBGTGP5R3A65B7E46RQ3M27","short_pith_number":"pith:QPGUBGTG","schema_version":"1.0","canonical_sha256":"83cd409a667f63b07ba1f939e8c36cd7fbcbf4edb24a344beddcecf41e5d7bdd","source":{"kind":"arxiv","id":"2211.00980","version":4},"attestation_state":"computed","paper":{"title":"Balancing Utility and Fairness in Submodular Maximization (Technical Report)","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DS","authors_text":"Francesco Bonchi, Yanhao Wang, Ying Wang, Yuchen Li","submitted_at":"2022-11-02T09:38:08Z","abstract_excerpt":"Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the \\emph{utility} and \\emph{fairness} ob"},"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":"2211.00980","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2022-11-02T09:38:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7d93c10083ce73786a00fa79e8e543a1887d55646f44ec7c9d9623b6d1a5bae8","abstract_canon_sha256":"ab75bd0737cad9e9d4b867f37321b6de5b481dc140f34056c9e9953bd7f953de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:46:44.557505Z","signature_b64":"jlBStmsDVBhPSzyiI+Sp23Z8sIRr/ptEB8LJCbdOt+ENXXoXRkWvVfOX6geK5g9CSDEXafdQ5+8AWbjsor4DBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83cd409a667f63b07ba1f939e8c36cd7fbcbf4edb24a344beddcecf41e5d7bdd","last_reissued_at":"2026-07-05T06:46:44.556973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:46:44.556973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Balancing Utility and Fairness in Submodular Maximization (Technical Report)","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DS","authors_text":"Francesco Bonchi, Yanhao Wang, Ying Wang, Yuchen Li","submitted_at":"2022-11-02T09:38:08Z","abstract_excerpt":"Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the \\emph{utility} and \\emph{fairness} ob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00980","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.00980/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2211.00980","created_at":"2026-07-05T06:46:44.557041+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.00980v4","created_at":"2026-07-05T06:46:44.557041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00980","created_at":"2026-07-05T06:46:44.557041+00:00"},{"alias_kind":"pith_short_12","alias_value":"QPGUBGTGP5R3","created_at":"2026-07-05T06:46:44.557041+00:00"},{"alias_kind":"pith_short_16","alias_value":"QPGUBGTGP5R3A65B","created_at":"2026-07-05T06:46:44.557041+00:00"},{"alias_kind":"pith_short_8","alias_value":"QPGUBGTG","created_at":"2026-07-05T06:46:44.557041+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.08758","citing_title":"On Fair Epsilon Net and Geometric Hitting Set","ref_index":50,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27","json":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27.json","graph_json":"https://pith.science/api/pith-number/QPGUBGTGP5R3A65B7E46RQ3M27/graph.json","events_json":"https://pith.science/api/pith-number/QPGUBGTGP5R3A65B7E46RQ3M27/events.json","paper":"https://pith.science/paper/QPGUBGTG"},"agent_actions":{"view_html":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27","download_json":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27.json","view_paper":"https://pith.science/paper/QPGUBGTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.00980&json=true","fetch_graph":"https://pith.science/api/pith-number/QPGUBGTGP5R3A65B7E46RQ3M27/graph.json","fetch_events":"https://pith.science/api/pith-number/QPGUBGTGP5R3A65B7E46RQ3M27/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27/action/storage_attestation","attest_author":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27/action/author_attestation","sign_citation":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27/action/citation_signature","submit_replication":"https://pith.science/pith/QPGUBGTGP5R3A65B7E46RQ3M27/action/replication_record"}},"created_at":"2026-07-05T06:46:44.557041+00:00","updated_at":"2026-07-05T06:46:44.557041+00:00"}