{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OSXCVYVXC7SCMSPYJTIH3UKBFS","short_pith_number":"pith:OSXCVYVX","schema_version":"1.0","canonical_sha256":"74ae2ae2b717e42649f84cd07dd1412cb4599baa287cb7278971c4d289f55722","source":{"kind":"arxiv","id":"1808.04855","version":1},"attestation_state":"computed","paper":{"title":"On spectral embedding performance and elucidating network structure in stochastic block model graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Carey E. Priebe, Joshua Cape, Minh Tang","submitted_at":"2018-08-14T18:51:52Z","abstract_excerpt":"Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations, such as the graph Laplacian or adjacency matrix. In this paper, we analyze the asymptotic information-theoretic relative performance of Laplacian spectral embedding and adjacency spectral embedding for block assignment recovery in stochastic block model graphs by way of Chernoff information. We investigate the relationship between spectral embedding performance and underlying network structure (e.g.~homogeneity, affinity, core-periphery, (un)balancedn"},"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":"1808.04855","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-08-14T18:51:52Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"fa24a444354472e14d145fb952e946914882c970d8e4bdd6d95b4ac320824ebc","abstract_canon_sha256":"a028d3628d5018ffb32521bd8140fdd52fb0047c40deabedc467f5cfad70a349"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:02.379272Z","signature_b64":"CgUy91qXvITPDRW3HQnE7LaxIGDGkZ+nRaA4xaR4qcJPpe/NoWowAxr7RtPNmVKu5RQNQiDJw/E40b3EMFDjAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74ae2ae2b717e42649f84cd07dd1412cb4599baa287cb7278971c4d289f55722","last_reissued_at":"2026-05-18T00:08:02.378738Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:02.378738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On spectral embedding performance and elucidating network structure in stochastic block model graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Carey E. Priebe, Joshua Cape, Minh Tang","submitted_at":"2018-08-14T18:51:52Z","abstract_excerpt":"Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations, such as the graph Laplacian or adjacency matrix. In this paper, we analyze the asymptotic information-theoretic relative performance of Laplacian spectral embedding and adjacency spectral embedding for block assignment recovery in stochastic block model graphs by way of Chernoff information. We investigate the relationship between spectral embedding performance and underlying network structure (e.g.~homogeneity, affinity, core-periphery, (un)balancedn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04855","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":"1808.04855","created_at":"2026-05-18T00:08:02.378834+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04855v1","created_at":"2026-05-18T00:08:02.378834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04855","created_at":"2026-05-18T00:08:02.378834+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSXCVYVXC7SC","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSXCVYVXC7SCMSPY","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSXCVYVX","created_at":"2026-05-18T12:32:43.782077+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/OSXCVYVXC7SCMSPYJTIH3UKBFS","json":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS.json","graph_json":"https://pith.science/api/pith-number/OSXCVYVXC7SCMSPYJTIH3UKBFS/graph.json","events_json":"https://pith.science/api/pith-number/OSXCVYVXC7SCMSPYJTIH3UKBFS/events.json","paper":"https://pith.science/paper/OSXCVYVX"},"agent_actions":{"view_html":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS","download_json":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS.json","view_paper":"https://pith.science/paper/OSXCVYVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04855&json=true","fetch_graph":"https://pith.science/api/pith-number/OSXCVYVXC7SCMSPYJTIH3UKBFS/graph.json","fetch_events":"https://pith.science/api/pith-number/OSXCVYVXC7SCMSPYJTIH3UKBFS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS/action/storage_attestation","attest_author":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS/action/author_attestation","sign_citation":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS/action/citation_signature","submit_replication":"https://pith.science/pith/OSXCVYVXC7SCMSPYJTIH3UKBFS/action/replication_record"}},"created_at":"2026-05-18T00:08:02.378834+00:00","updated_at":"2026-05-18T00:08:02.378834+00:00"}