{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6KS2O333R7UMHJOIHPX5E7TA5U","short_pith_number":"pith:6KS2O333","canonical_record":{"source":{"id":"2501.06002","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T14:34:20Z","cross_cats_sorted":[],"title_canon_sha256":"733bc4bd9581fe008b7cf3d4a0584f2c4f02bf455b41f07dd94f5473d59f0cbf","abstract_canon_sha256":"c0b97ea6a46ca7fe2a37e6a84aa7bed37fa3ff9245a7ade865539e7fbb76332f"},"schema_version":"1.0"},"canonical_sha256":"f2a5a76f7b8fe8c3a5c83befd27e60ed0611cfd8d8b317ddcec2c051561817ab","source":{"kind":"arxiv","id":"2501.06002","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.06002","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"arxiv_version","alias_value":"2501.06002v1","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06002","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"pith_short_12","alias_value":"6KS2O333R7UM","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"pith_short_16","alias_value":"6KS2O333R7UMHJOI","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"pith_short_8","alias_value":"6KS2O333","created_at":"2026-07-05T09:59:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6KS2O333R7UMHJOIHPX5E7TA5U","target":"record","payload":{"canonical_record":{"source":{"id":"2501.06002","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T14:34:20Z","cross_cats_sorted":[],"title_canon_sha256":"733bc4bd9581fe008b7cf3d4a0584f2c4f02bf455b41f07dd94f5473d59f0cbf","abstract_canon_sha256":"c0b97ea6a46ca7fe2a37e6a84aa7bed37fa3ff9245a7ade865539e7fbb76332f"},"schema_version":"1.0"},"canonical_sha256":"f2a5a76f7b8fe8c3a5c83befd27e60ed0611cfd8d8b317ddcec2c051561817ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:31.393685Z","signature_b64":"HKunLUiD02TvVvA5xyD4NGl5HxNauwdq63qzmxvCpreLLls5cOZa0sKs0a+cWr/80lc4Oy/PIZSg4fQ3S/iABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2a5a76f7b8fe8c3a5c83befd27e60ed0611cfd8d8b317ddcec2c051561817ab","last_reissued_at":"2026-07-05T09:59:31.393253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:31.393253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.06002","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:59:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NocBaxYcWTf+M7sGY2cy/1PNlmhxYYCvo7mGiHe797BGkmBaajyUj3Ym7gFjql0xCFfl+6VK+dld+9GAnD+gDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T16:14:20.888988Z"},"content_sha256":"535ca98dc5e802c31d8249b8f85465fbb63639aeb8cb0b8466af6a585e692450","schema_version":"1.0","event_id":"sha256:535ca98dc5e802c31d8249b8f85465fbb63639aeb8cb0b8466af6a585e692450"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6KS2O333R7UMHJOIHPX5E7TA5U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeltaGNN: Graph Neural Network with Information Flow Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Islem Rekik, Kevin Mancini","submitted_at":"2025-01-10T14:34:20Z","abstract_excerpt":"Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process. When applied to semi-supervised node classification, the message-passing enables GNNs to understand short-range spatial interactions, but also causes them to suffer from over-smoothing and over-squashing. These challenges hinder model expressiveness and prevent the use of deeper models to capture long-range node interactions (LRIs) within the graph. Popular solutions for LRIs detection are either too expensive to pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06002","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/2501.06002/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":"2026-07-05T09:59:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5GrjAeAevECxrvT130oOuQJVh7SDmcIWSbdHExolVG77nQG+rCKwCS5cTuxPjDCedEsx7elNAi83n+RqZ4kCCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T16:14:20.889333Z"},"content_sha256":"e174177aac6f7197ad3e6afccf767e2bcf477ab2affdd6dc8b1d8e982e29fe47","schema_version":"1.0","event_id":"sha256:e174177aac6f7197ad3e6afccf767e2bcf477ab2affdd6dc8b1d8e982e29fe47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6KS2O333R7UMHJOIHPX5E7TA5U/bundle.json","state_url":"https://pith.science/pith/6KS2O333R7UMHJOIHPX5E7TA5U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6KS2O333R7UMHJOIHPX5E7TA5U/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":"2026-08-11T16:14:20Z","links":{"resolver":"https://pith.science/pith/6KS2O333R7UMHJOIHPX5E7TA5U","bundle":"https://pith.science/pith/6KS2O333R7UMHJOIHPX5E7TA5U/bundle.json","state":"https://pith.science/pith/6KS2O333R7UMHJOIHPX5E7TA5U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6KS2O333R7UMHJOIHPX5E7TA5U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6KS2O333R7UMHJOIHPX5E7TA5U","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":"c0b97ea6a46ca7fe2a37e6a84aa7bed37fa3ff9245a7ade865539e7fbb76332f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T14:34:20Z","title_canon_sha256":"733bc4bd9581fe008b7cf3d4a0584f2c4f02bf455b41f07dd94f5473d59f0cbf"},"schema_version":"1.0","source":{"id":"2501.06002","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.06002","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"arxiv_version","alias_value":"2501.06002v1","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06002","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"pith_short_12","alias_value":"6KS2O333R7UM","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"pith_short_16","alias_value":"6KS2O333R7UMHJOI","created_at":"2026-07-05T09:59:31Z"},{"alias_kind":"pith_short_8","alias_value":"6KS2O333","created_at":"2026-07-05T09:59:31Z"}],"graph_snapshots":[{"event_id":"sha256:e174177aac6f7197ad3e6afccf767e2bcf477ab2affdd6dc8b1d8e982e29fe47","target":"graph","created_at":"2026-07-05T09:59:31Z","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/2501.06002/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process. When applied to semi-supervised node classification, the message-passing enables GNNs to understand short-range spatial interactions, but also causes them to suffer from over-smoothing and over-squashing. These challenges hinder model expressiveness and prevent the use of deeper models to capture long-range node interactions (LRIs) within the graph. Popular solutions for LRIs detection are either too expensive to pro","authors_text":"Islem Rekik, Kevin Mancini","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T14:34:20Z","title":"DeltaGNN: Graph Neural Network with Information Flow Control"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06002","kind":"arxiv","version":1},"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":"sha256:535ca98dc5e802c31d8249b8f85465fbb63639aeb8cb0b8466af6a585e692450","target":"record","created_at":"2026-07-05T09:59:31Z","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":"c0b97ea6a46ca7fe2a37e6a84aa7bed37fa3ff9245a7ade865539e7fbb76332f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T14:34:20Z","title_canon_sha256":"733bc4bd9581fe008b7cf3d4a0584f2c4f02bf455b41f07dd94f5473d59f0cbf"},"schema_version":"1.0","source":{"id":"2501.06002","kind":"arxiv","version":1}},"canonical_sha256":"f2a5a76f7b8fe8c3a5c83befd27e60ed0611cfd8d8b317ddcec2c051561817ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2a5a76f7b8fe8c3a5c83befd27e60ed0611cfd8d8b317ddcec2c051561817ab","first_computed_at":"2026-07-05T09:59:31.393253Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:59:31.393253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HKunLUiD02TvVvA5xyD4NGl5HxNauwdq63qzmxvCpreLLls5cOZa0sKs0a+cWr/80lc4Oy/PIZSg4fQ3S/iABA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:59:31.393685Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.06002","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:535ca98dc5e802c31d8249b8f85465fbb63639aeb8cb0b8466af6a585e692450","sha256:e174177aac6f7197ad3e6afccf767e2bcf477ab2affdd6dc8b1d8e982e29fe47"],"state_sha256":"f952e5048617d38a4755bdafe04c561cef52b957316629dc100cf446c84e9e9a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gTIS3vMmYQLYzn6Hq/6dRMLueqm22WO5ozkakzaXVswbz2VmzHs4PSqaC3XbJQoGfZg7EaTEofmCD7Rgf6lNBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T16:14:20.892002Z","bundle_sha256":"5bed56b2bbf5b655b6717b3cab7addba56c155c9f2be76865e11de1c92c4605f"}}