{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PVIQUF2EOSRVJQNVOAYKW4KPQV","short_pith_number":"pith:PVIQUF2E","schema_version":"1.0","canonical_sha256":"7d510a174474a354c1b57030ab714f85502a9502ba96d1e1fe8e6231fde8e2d7","source":{"kind":"arxiv","id":"1901.03466","version":1},"attestation_state":"computed","paper":{"title":"Efficient Sampling for Selecting Important Nodes in Random Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"stat.ME","authors_text":"Chun-Hung Chen, Haidong Li, Xiaoyun Xu, Yijie Peng","submitted_at":"2019-01-11T03:16:30Z","abstract_excerpt":"We consider the problem of selecting important nodes in a random network, where the nodes connect to each other randomly with certain transition probabilities. The node importance is characterized by the stationary probabilities of the corresponding nodes in a Markov chain defined over the network, as in Google's PageRank. Unlike deterministic network, the transition probabilities in random network are unknown but can be estimated by sampling. Under a Bayesian learning framework, we apply the first-order Taylor expansion and normal approximation to provide a computationally efficient posterior"},"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":"1901.03466","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-01-11T03:16:30Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"79eff5102161293480ef1da4691c505ba464a19ff09d994a4b30fc71cad1cd4a","abstract_canon_sha256":"878ee4c7861dee886fcb86dfa0e2ef21b50c32866e2151282024a763bf6183a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:31.180876Z","signature_b64":"x+ATL1PxO7Ia9/Pz0Cc9xOSzzdgFK0Xfv20XKXUSmFsF1XDGf0zSji6tHglhDop2G0IOFTt7uZ6AHlgzUMpEAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d510a174474a354c1b57030ab714f85502a9502ba96d1e1fe8e6231fde8e2d7","last_reissued_at":"2026-05-17T23:56:31.180477Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:31.180477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Sampling for Selecting Important Nodes in Random Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"stat.ME","authors_text":"Chun-Hung Chen, Haidong Li, Xiaoyun Xu, Yijie Peng","submitted_at":"2019-01-11T03:16:30Z","abstract_excerpt":"We consider the problem of selecting important nodes in a random network, where the nodes connect to each other randomly with certain transition probabilities. The node importance is characterized by the stationary probabilities of the corresponding nodes in a Markov chain defined over the network, as in Google's PageRank. Unlike deterministic network, the transition probabilities in random network are unknown but can be estimated by sampling. Under a Bayesian learning framework, we apply the first-order Taylor expansion and normal approximation to provide a computationally efficient posterior"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03466","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":"1901.03466","created_at":"2026-05-17T23:56:31.180537+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.03466v1","created_at":"2026-05-17T23:56:31.180537+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03466","created_at":"2026-05-17T23:56:31.180537+00:00"},{"alias_kind":"pith_short_12","alias_value":"PVIQUF2EOSRV","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PVIQUF2EOSRVJQNV","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PVIQUF2E","created_at":"2026-05-18T12:33:24.271573+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/PVIQUF2EOSRVJQNVOAYKW4KPQV","json":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV.json","graph_json":"https://pith.science/api/pith-number/PVIQUF2EOSRVJQNVOAYKW4KPQV/graph.json","events_json":"https://pith.science/api/pith-number/PVIQUF2EOSRVJQNVOAYKW4KPQV/events.json","paper":"https://pith.science/paper/PVIQUF2E"},"agent_actions":{"view_html":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV","download_json":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV.json","view_paper":"https://pith.science/paper/PVIQUF2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.03466&json=true","fetch_graph":"https://pith.science/api/pith-number/PVIQUF2EOSRVJQNVOAYKW4KPQV/graph.json","fetch_events":"https://pith.science/api/pith-number/PVIQUF2EOSRVJQNVOAYKW4KPQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV/action/storage_attestation","attest_author":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV/action/author_attestation","sign_citation":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV/action/citation_signature","submit_replication":"https://pith.science/pith/PVIQUF2EOSRVJQNVOAYKW4KPQV/action/replication_record"}},"created_at":"2026-05-17T23:56:31.180537+00:00","updated_at":"2026-05-17T23:56:31.180537+00:00"}