{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:NCBU6F5BPYG2PYZYSHJORKWOJA","short_pith_number":"pith:NCBU6F5B","schema_version":"1.0","canonical_sha256":"68834f17a17e0da7e33891d2e8aace4829c1da0b99e662fa68b46793c5d3cd43","source":{"kind":"arxiv","id":"2001.04938","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric regression for multiple heterogeneous networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Pierre-Andre Maugis, Swati Chandna","submitted_at":"2020-01-14T17:54:59Z","abstract_excerpt":"We study nonparametric methods for the setting where multiple distinct networks are observed on the same set of nodes. Such samples may arise in the form of replicated networks drawn from a common distribution, or in the form of heterogeneous networks, with the network generating process varying from one network to another, e.g.~dynamic and cross-sectional networks. Nonparametric methods for undirected networks have focused on estimation of the graphon model. While the graphon model accounts for nodal heterogeneity, it does not account for network heterogeneity, a feature specific to applicati"},"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":"2001.04938","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-01-14T17:54:59Z","cross_cats_sorted":[],"title_canon_sha256":"444fbb562bb23af1f836fa43cd739d0488613c218653b2c132f2962b670391d3","abstract_canon_sha256":"b2a03099022f371ea064ea11b695457b2a533c431603f1b77a121f1efd584941"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:31.257461Z","signature_b64":"vDfTZX8HP8WxtyflhiUzNHbi+X5nrNI8O5tHu4iKqp9Dm5Y/haQhgNHyOQWMbB5KTR8A/TvNcExrBLPgdNEgBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68834f17a17e0da7e33891d2e8aace4829c1da0b99e662fa68b46793c5d3cd43","last_reissued_at":"2026-07-05T00:33:31.257122Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:31.257122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric regression for multiple heterogeneous networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Pierre-Andre Maugis, Swati Chandna","submitted_at":"2020-01-14T17:54:59Z","abstract_excerpt":"We study nonparametric methods for the setting where multiple distinct networks are observed on the same set of nodes. Such samples may arise in the form of replicated networks drawn from a common distribution, or in the form of heterogeneous networks, with the network generating process varying from one network to another, e.g.~dynamic and cross-sectional networks. Nonparametric methods for undirected networks have focused on estimation of the graphon model. While the graphon model accounts for nodal heterogeneity, it does not account for network heterogeneity, a feature specific to applicati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.04938","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/2001.04938/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":"2001.04938","created_at":"2026-07-05T00:33:31.257178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.04938v1","created_at":"2026-07-05T00:33:31.257178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.04938","created_at":"2026-07-05T00:33:31.257178+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCBU6F5BPYG2","created_at":"2026-07-05T00:33:31.257178+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCBU6F5BPYG2PYZY","created_at":"2026-07-05T00:33:31.257178+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCBU6F5B","created_at":"2026-07-05T00:33:31.257178+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/NCBU6F5BPYG2PYZYSHJORKWOJA","json":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA.json","graph_json":"https://pith.science/api/pith-number/NCBU6F5BPYG2PYZYSHJORKWOJA/graph.json","events_json":"https://pith.science/api/pith-number/NCBU6F5BPYG2PYZYSHJORKWOJA/events.json","paper":"https://pith.science/paper/NCBU6F5B"},"agent_actions":{"view_html":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA","download_json":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA.json","view_paper":"https://pith.science/paper/NCBU6F5B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.04938&json=true","fetch_graph":"https://pith.science/api/pith-number/NCBU6F5BPYG2PYZYSHJORKWOJA/graph.json","fetch_events":"https://pith.science/api/pith-number/NCBU6F5BPYG2PYZYSHJORKWOJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA/action/storage_attestation","attest_author":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA/action/author_attestation","sign_citation":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA/action/citation_signature","submit_replication":"https://pith.science/pith/NCBU6F5BPYG2PYZYSHJORKWOJA/action/replication_record"}},"created_at":"2026-07-05T00:33:31.257178+00:00","updated_at":"2026-07-05T00:33:31.257178+00:00"}