{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QIWUGMN4EI26WRGPD4O6GSUH3F","short_pith_number":"pith:QIWUGMN4","canonical_record":{"source":{"id":"2309.02138","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-05T11:29:25Z","cross_cats_sorted":["cs.AI","math.AT"],"title_canon_sha256":"e135f60830ee7ef0fcaa67560893db3af8096dbdb8c37028fd9c915c0770f1f1","abstract_canon_sha256":"b49b7cd19f21b3b9580cb7a31503bffe5199c7f7238e7d5a8fa830aff63d2ba0"},"schema_version":"1.0"},"canonical_sha256":"822d4331bc2235eb44cf1f1de34a87d968a388494f2c4d4651c4b18d8843b518","source":{"kind":"arxiv","id":"2309.02138","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.02138","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"arxiv_version","alias_value":"2309.02138v2","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02138","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"pith_short_12","alias_value":"QIWUGMN4EI26","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"pith_short_16","alias_value":"QIWUGMN4EI26WRGP","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"pith_short_8","alias_value":"QIWUGMN4","created_at":"2026-07-05T09:20:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QIWUGMN4EI26WRGPD4O6GSUH3F","target":"record","payload":{"canonical_record":{"source":{"id":"2309.02138","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-05T11:29:25Z","cross_cats_sorted":["cs.AI","math.AT"],"title_canon_sha256":"e135f60830ee7ef0fcaa67560893db3af8096dbdb8c37028fd9c915c0770f1f1","abstract_canon_sha256":"b49b7cd19f21b3b9580cb7a31503bffe5199c7f7238e7d5a8fa830aff63d2ba0"},"schema_version":"1.0"},"canonical_sha256":"822d4331bc2235eb44cf1f1de34a87d968a388494f2c4d4651c4b18d8843b518","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:37.420703Z","signature_b64":"CpDuPIMuJgC0Zabt0O9qGJoCATCZ73g7fCfdHM5vtb0nSy8vzAHquUMG9ABXuX4czs+U4zic4E6Xpqae+meEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"822d4331bc2235eb44cf1f1de34a87d968a388494f2c4d4651c4b18d8843b518","last_reissued_at":"2026-07-05T09:20:37.420234Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:37.420234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.02138","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:20:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IHQEXkwCaOXn5NtiQpGxKd6F3ZDEM63gQdggj4baC0KE4JZ1gtup5dhe9KNmR3N2ufeI004VMIgiS2UrvjeFDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:28:01.876259Z"},"content_sha256":"a9b3943a25392653232b22e70c8f7e03afa0ff67ae6220c2e40a74827c1fea5f","schema_version":"1.0","event_id":"sha256:a9b3943a25392653232b22e70c8f7e03afa0ff67ae6220c2e40a74827c1fea5f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QIWUGMN4EI26WRGPD4O6GSUH3F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalized Simplicial Attention Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.AT"],"primary_cat":"cs.LG","authors_text":"Claudio Battiloro, Lorenzo Giusti, Lucia Testa, Paolo Di Lorenzo, Sergio Barbarossa, Stefania Sardellitti","submitted_at":"2023-09-05T11:29:25Z","abstract_excerpt":"Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes. For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers. Hinging on topological si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02138","kind":"arxiv","version":2},"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/2309.02138/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:20:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VYz5GT2sNdcp4N+MQE/67+mjs4aDoGivOXuFUem3/yTWjvj4NykXAvyozok43cH1M3UDvFFav9i5s3AoLMhTBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:28:01.876781Z"},"content_sha256":"b5f810f21e15937a4d55dd48995d0d19d30ffb9c72fc069565f3923c5e8dd6cb","schema_version":"1.0","event_id":"sha256:b5f810f21e15937a4d55dd48995d0d19d30ffb9c72fc069565f3923c5e8dd6cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QIWUGMN4EI26WRGPD4O6GSUH3F/bundle.json","state_url":"https://pith.science/pith/QIWUGMN4EI26WRGPD4O6GSUH3F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QIWUGMN4EI26WRGPD4O6GSUH3F/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T03:28:01Z","links":{"resolver":"https://pith.science/pith/QIWUGMN4EI26WRGPD4O6GSUH3F","bundle":"https://pith.science/pith/QIWUGMN4EI26WRGPD4O6GSUH3F/bundle.json","state":"https://pith.science/pith/QIWUGMN4EI26WRGPD4O6GSUH3F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QIWUGMN4EI26WRGPD4O6GSUH3F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QIWUGMN4EI26WRGPD4O6GSUH3F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b49b7cd19f21b3b9580cb7a31503bffe5199c7f7238e7d5a8fa830aff63d2ba0","cross_cats_sorted":["cs.AI","math.AT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-05T11:29:25Z","title_canon_sha256":"e135f60830ee7ef0fcaa67560893db3af8096dbdb8c37028fd9c915c0770f1f1"},"schema_version":"1.0","source":{"id":"2309.02138","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.02138","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"arxiv_version","alias_value":"2309.02138v2","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02138","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"pith_short_12","alias_value":"QIWUGMN4EI26","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"pith_short_16","alias_value":"QIWUGMN4EI26WRGP","created_at":"2026-07-05T09:20:37Z"},{"alias_kind":"pith_short_8","alias_value":"QIWUGMN4","created_at":"2026-07-05T09:20:37Z"}],"graph_snapshots":[{"event_id":"sha256:b5f810f21e15937a4d55dd48995d0d19d30ffb9c72fc069565f3923c5e8dd6cb","target":"graph","created_at":"2026-07-05T09:20:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.02138/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes. For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers. Hinging on topological si","authors_text":"Claudio Battiloro, Lorenzo Giusti, Lucia Testa, Paolo Di Lorenzo, Sergio Barbarossa, Stefania Sardellitti","cross_cats":["cs.AI","math.AT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-05T11:29:25Z","title":"Generalized Simplicial Attention Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02138","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a9b3943a25392653232b22e70c8f7e03afa0ff67ae6220c2e40a74827c1fea5f","target":"record","created_at":"2026-07-05T09:20:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b49b7cd19f21b3b9580cb7a31503bffe5199c7f7238e7d5a8fa830aff63d2ba0","cross_cats_sorted":["cs.AI","math.AT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-09-05T11:29:25Z","title_canon_sha256":"e135f60830ee7ef0fcaa67560893db3af8096dbdb8c37028fd9c915c0770f1f1"},"schema_version":"1.0","source":{"id":"2309.02138","kind":"arxiv","version":2}},"canonical_sha256":"822d4331bc2235eb44cf1f1de34a87d968a388494f2c4d4651c4b18d8843b518","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"822d4331bc2235eb44cf1f1de34a87d968a388494f2c4d4651c4b18d8843b518","first_computed_at":"2026-07-05T09:20:37.420234Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:20:37.420234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CpDuPIMuJgC0Zabt0O9qGJoCATCZ73g7fCfdHM5vtb0nSy8vzAHquUMG9ABXuX4czs+U4zic4E6Xpqae+meEDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:20:37.420703Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.02138","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a9b3943a25392653232b22e70c8f7e03afa0ff67ae6220c2e40a74827c1fea5f","sha256:b5f810f21e15937a4d55dd48995d0d19d30ffb9c72fc069565f3923c5e8dd6cb"],"state_sha256":"3eb36e28e4cc7ac548be511c4840ebe35053ddbb637331e4e23fd737a9078c6a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mKZLYjNCJc3xJLiU7x+UcTP/iSC/UdDbKt5fleqf4HMGJEL/h8KhnvOa1srzOhHjLq0DZzFFODnIS0kCLIQVAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:28:01.882395Z","bundle_sha256":"0e5bfbc41983e1e1459601655101829c456aac14156e39aa83f64922dcf0c77c"}}