{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3W66UYFZN4BSN6Y2G3SC47HBBU","short_pith_number":"pith:3W66UYFZ","schema_version":"1.0","canonical_sha256":"ddbdea60b96f0326fb1a36e42e7ce10d24c67e946a0269d191ea8c278777ac30","source":{"kind":"arxiv","id":"2605.19733","version":1},"attestation_state":"computed","paper":{"title":"Graph Neural Networks for Community Detection in Graph Signal Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Alessandra De Rossi, Enrico Montini, Roberto Cavoretto","submitted_at":"2026-05-19T12:07:51Z","abstract_excerpt":"Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years, Graph Neural Networks (GNNs) have emerged as effective tools for learning low-dimensional representations of graph-structured data and have shown strong performance in clustering tasks, particularly on large and high-dimensional graphs. This paper investigates the use of GNN-based community detection within a graph signal interpolation framework. After reviewing the main classes of GNN architectures for community detection according to a standa"},"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":"2605.19733","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-05-19T12:07:51Z","cross_cats_sorted":["cs.LG","cs.NA"],"title_canon_sha256":"b1362dd627ccb7cae06ea0d4b5d807b4b919ab17fc9cc8cdf207a461e1f9946b","abstract_canon_sha256":"03c6255e1682921bc2a079c360512e3273a3567519add36c211bdd782b24ed32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:06:11.472744Z","signature_b64":"Ye6Qnf8fINjzFybkdIKrFfQuPVWJkLvYca6k3fu9Hp8brXdhuMEHvLSwlqVoXR+Hqhsw/Oh0bROdPKvvnYxnDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddbdea60b96f0326fb1a36e42e7ce10d24c67e946a0269d191ea8c278777ac30","last_reissued_at":"2026-05-20T01:06:11.472010Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:06:11.472010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural Networks for Community Detection in Graph Signal Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Alessandra De Rossi, Enrico Montini, Roberto Cavoretto","submitted_at":"2026-05-19T12:07:51Z","abstract_excerpt":"Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years, Graph Neural Networks (GNNs) have emerged as effective tools for learning low-dimensional representations of graph-structured data and have shown strong performance in clustering tasks, particularly on large and high-dimensional graphs. This paper investigates the use of GNN-based community detection within a graph signal interpolation framework. After reviewing the main classes of GNN architectures for community detection according to a standa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19733","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/2605.19733/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":"2605.19733","created_at":"2026-05-20T01:06:11.472119+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.19733v1","created_at":"2026-05-20T01:06:11.472119+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19733","created_at":"2026-05-20T01:06:11.472119+00:00"},{"alias_kind":"pith_short_12","alias_value":"3W66UYFZN4BS","created_at":"2026-05-20T01:06:11.472119+00:00"},{"alias_kind":"pith_short_16","alias_value":"3W66UYFZN4BSN6Y2","created_at":"2026-05-20T01:06:11.472119+00:00"},{"alias_kind":"pith_short_8","alias_value":"3W66UYFZ","created_at":"2026-05-20T01:06:11.472119+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/3W66UYFZN4BSN6Y2G3SC47HBBU","json":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU.json","graph_json":"https://pith.science/api/pith-number/3W66UYFZN4BSN6Y2G3SC47HBBU/graph.json","events_json":"https://pith.science/api/pith-number/3W66UYFZN4BSN6Y2G3SC47HBBU/events.json","paper":"https://pith.science/paper/3W66UYFZ"},"agent_actions":{"view_html":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU","download_json":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU.json","view_paper":"https://pith.science/paper/3W66UYFZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.19733&json=true","fetch_graph":"https://pith.science/api/pith-number/3W66UYFZN4BSN6Y2G3SC47HBBU/graph.json","fetch_events":"https://pith.science/api/pith-number/3W66UYFZN4BSN6Y2G3SC47HBBU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU/action/storage_attestation","attest_author":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU/action/author_attestation","sign_citation":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU/action/citation_signature","submit_replication":"https://pith.science/pith/3W66UYFZN4BSN6Y2G3SC47HBBU/action/replication_record"}},"created_at":"2026-05-20T01:06:11.472119+00:00","updated_at":"2026-05-20T01:06:11.472119+00:00"}