{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WYW57I2UPUTHDRJGXGGRUJXB5J","short_pith_number":"pith:WYW57I2U","schema_version":"1.0","canonical_sha256":"b62ddfa3547d2671c526b98d1a26e1ea7cb923536fb69d308ebbe18e324a6ec6","source":{"kind":"arxiv","id":"2606.22241","version":1},"attestation_state":"computed","paper":{"title":"Efficient Algorithms for Influence Maximization in General Models and Observed Cascades","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Alina Ene, Fabian Spaeh, Huy L. Nguyen, Themistoklis Haris","submitted_at":"2026-06-20T21:58:01Z","abstract_excerpt":"We study influence maximization in general stochastic models, the observed cascades model, and the independent cascade (IC) model. For general stochastic models with only black-box sample access, we introduce a low-adaptivity optimization framework that improves sample complexity and running time over Sadeh et al. (2020) and is instrumental to all our results. We further introduce an adaptive algorithm guided by empirical variance, avoiding pessimistic worst-case bounds. Combining our optimization framework with sketching, we obtain the first algorithm with provable guarantees and nearly-linea"},"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":"2606.22241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2026-06-20T21:58:01Z","cross_cats_sorted":[],"title_canon_sha256":"de61690475e80727302bce322e822435be47e2a91a4df7945a7a9b713713c9fd","abstract_canon_sha256":"094224b66ffe9fcee17d3299c92cc94ea4cb3622ae6638e5b5854600bc1c579b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:32.734932Z","signature_b64":"PBFMcLQe7bHGf/XIC8D5C29odmNliyFqMu6TrGhfch9bm9RrALTRAyACXT7U9dyGYzDcRcZKaB/8JFQt8hBuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b62ddfa3547d2671c526b98d1a26e1ea7cb923536fb69d308ebbe18e324a6ec6","last_reissued_at":"2026-06-23T02:13:32.734561Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:32.734561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Algorithms for Influence Maximization in General Models and Observed Cascades","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Alina Ene, Fabian Spaeh, Huy L. Nguyen, Themistoklis Haris","submitted_at":"2026-06-20T21:58:01Z","abstract_excerpt":"We study influence maximization in general stochastic models, the observed cascades model, and the independent cascade (IC) model. For general stochastic models with only black-box sample access, we introduce a low-adaptivity optimization framework that improves sample complexity and running time over Sadeh et al. (2020) and is instrumental to all our results. We further introduce an adaptive algorithm guided by empirical variance, avoiding pessimistic worst-case bounds. Combining our optimization framework with sketching, we obtain the first algorithm with provable guarantees and nearly-linea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.22241","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.22241/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":"2606.22241","created_at":"2026-06-23T02:13:32.734611+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.22241v1","created_at":"2026-06-23T02:13:32.734611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.22241","created_at":"2026-06-23T02:13:32.734611+00:00"},{"alias_kind":"pith_short_12","alias_value":"WYW57I2UPUTH","created_at":"2026-06-23T02:13:32.734611+00:00"},{"alias_kind":"pith_short_16","alias_value":"WYW57I2UPUTHDRJG","created_at":"2026-06-23T02:13:32.734611+00:00"},{"alias_kind":"pith_short_8","alias_value":"WYW57I2U","created_at":"2026-06-23T02:13:32.734611+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/WYW57I2UPUTHDRJGXGGRUJXB5J","json":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J.json","graph_json":"https://pith.science/api/pith-number/WYW57I2UPUTHDRJGXGGRUJXB5J/graph.json","events_json":"https://pith.science/api/pith-number/WYW57I2UPUTHDRJGXGGRUJXB5J/events.json","paper":"https://pith.science/paper/WYW57I2U"},"agent_actions":{"view_html":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J","download_json":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J.json","view_paper":"https://pith.science/paper/WYW57I2U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.22241&json=true","fetch_graph":"https://pith.science/api/pith-number/WYW57I2UPUTHDRJGXGGRUJXB5J/graph.json","fetch_events":"https://pith.science/api/pith-number/WYW57I2UPUTHDRJGXGGRUJXB5J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J/action/storage_attestation","attest_author":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J/action/author_attestation","sign_citation":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J/action/citation_signature","submit_replication":"https://pith.science/pith/WYW57I2UPUTHDRJGXGGRUJXB5J/action/replication_record"}},"created_at":"2026-06-23T02:13:32.734611+00:00","updated_at":"2026-06-23T02:13:32.734611+00:00"}