{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:P24AX55YFHUTTPIAMSAWKZ6PHS","short_pith_number":"pith:P24AX55Y","schema_version":"1.0","canonical_sha256":"7eb80bf7b829e939bd0064816567cf3c88b133910f1e1aecb9640f197482568c","source":{"kind":"arxiv","id":"2506.04206","version":1},"attestation_state":"computed","paper":{"title":"A Few Moments Please: Scalable Graphon Learning via Moment Matching","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antonio G. Marques, Ashutosh Sabharwal, Reza Ramezanpour, Santiago Segarra, Victor M. Tenorio","submitted_at":"2025-06-04T17:51:01Z","abstract_excerpt":"Graphons, as limit objects of dense graph sequences, play a central role in the statistical analysis of network data. However, existing graphon estimation methods often struggle with scalability to large networks and resolution-independent approximation, due to their reliance on estimating latent variables or costly metrics such as the Gromov-Wasserstein distance. In this work, we propose a novel, scalable graphon estimator that directly recovers the graphon via moment matching, leveraging implicit neural representations (INRs). Our approach avoids latent variable modeling by training an INR--"},"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":"2506.04206","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T17:51:01Z","cross_cats_sorted":[],"title_canon_sha256":"fd86dbbf04d2ad1e7df890d743464b0f6aca330a2c4d7b3b5b546042eb247dda","abstract_canon_sha256":"011476680a123ec43a989e5f7bd813a677f73f1fcfc153c3cb17db6d9726c296"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:58.908646Z","signature_b64":"dsD00EFBH2CAMggiyAq9M3/270TD0gFwdqYpVE30LN8dHflQjcBhWzFmZXd33eMn+axvfAgPLDY2klbJBrmOCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7eb80bf7b829e939bd0064816567cf3c88b133910f1e1aecb9640f197482568c","last_reissued_at":"2026-07-05T11:15:58.908162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:58.908162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Few Moments Please: Scalable Graphon Learning via Moment Matching","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antonio G. Marques, Ashutosh Sabharwal, Reza Ramezanpour, Santiago Segarra, Victor M. Tenorio","submitted_at":"2025-06-04T17:51:01Z","abstract_excerpt":"Graphons, as limit objects of dense graph sequences, play a central role in the statistical analysis of network data. However, existing graphon estimation methods often struggle with scalability to large networks and resolution-independent approximation, due to their reliance on estimating latent variables or costly metrics such as the Gromov-Wasserstein distance. In this work, we propose a novel, scalable graphon estimator that directly recovers the graphon via moment matching, leveraging implicit neural representations (INRs). Our approach avoids latent variable modeling by training an INR--"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04206","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/2506.04206/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":"2506.04206","created_at":"2026-07-05T11:15:58.908218+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04206v1","created_at":"2026-07-05T11:15:58.908218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04206","created_at":"2026-07-05T11:15:58.908218+00:00"},{"alias_kind":"pith_short_12","alias_value":"P24AX55YFHUT","created_at":"2026-07-05T11:15:58.908218+00:00"},{"alias_kind":"pith_short_16","alias_value":"P24AX55YFHUTTPIA","created_at":"2026-07-05T11:15:58.908218+00:00"},{"alias_kind":"pith_short_8","alias_value":"P24AX55Y","created_at":"2026-07-05T11:15:58.908218+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/P24AX55YFHUTTPIAMSAWKZ6PHS","json":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS.json","graph_json":"https://pith.science/api/pith-number/P24AX55YFHUTTPIAMSAWKZ6PHS/graph.json","events_json":"https://pith.science/api/pith-number/P24AX55YFHUTTPIAMSAWKZ6PHS/events.json","paper":"https://pith.science/paper/P24AX55Y"},"agent_actions":{"view_html":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS","download_json":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS.json","view_paper":"https://pith.science/paper/P24AX55Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04206&json=true","fetch_graph":"https://pith.science/api/pith-number/P24AX55YFHUTTPIAMSAWKZ6PHS/graph.json","fetch_events":"https://pith.science/api/pith-number/P24AX55YFHUTTPIAMSAWKZ6PHS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS/action/storage_attestation","attest_author":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS/action/author_attestation","sign_citation":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS/action/citation_signature","submit_replication":"https://pith.science/pith/P24AX55YFHUTTPIAMSAWKZ6PHS/action/replication_record"}},"created_at":"2026-07-05T11:15:58.908218+00:00","updated_at":"2026-07-05T11:15:58.908218+00:00"}