{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GZKU3GLJNUBPIJJ2KDZ34IYVHI","short_pith_number":"pith:GZKU3GLJ","canonical_record":{"source":{"id":"2408.11370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-21T06:42:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"860d252f39fdf7c54934c0ce89f52664aa65e07547087b63c09acdb7211ab76e","abstract_canon_sha256":"43a1b0f07f2e20ff5a6392b52cad540984b476db133108d00d361ac230c98be5"},"schema_version":"1.0"},"canonical_sha256":"36554d99696d02f4253a50f3be23153a3f093c1b458b35b841164db4d305f9b4","source":{"kind":"arxiv","id":"2408.11370","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.11370","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"arxiv_version","alias_value":"2408.11370v1","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11370","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"pith_short_12","alias_value":"GZKU3GLJNUBP","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"pith_short_16","alias_value":"GZKU3GLJNUBPIJJ2","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"pith_short_8","alias_value":"GZKU3GLJ","created_at":"2026-07-05T08:57:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GZKU3GLJNUBPIJJ2KDZ34IYVHI","target":"record","payload":{"canonical_record":{"source":{"id":"2408.11370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-21T06:42:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"860d252f39fdf7c54934c0ce89f52664aa65e07547087b63c09acdb7211ab76e","abstract_canon_sha256":"43a1b0f07f2e20ff5a6392b52cad540984b476db133108d00d361ac230c98be5"},"schema_version":"1.0"},"canonical_sha256":"36554d99696d02f4253a50f3be23153a3f093c1b458b35b841164db4d305f9b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:42.793979Z","signature_b64":"7AZoiLovg/K5Rjn3LOIW7E9S6UZL8Q2ZVQWE1SHj6bsssRFvhywlTttO4xF+hTrxs4C7tW/pC8oOYSUT7VVWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36554d99696d02f4253a50f3be23153a3f093c1b458b35b841164db4d305f9b4","last_reissued_at":"2026-07-05T08:57:42.793517Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:42.793517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.11370","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":"2026-07-05T08:57:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gtVLEIjxlkCj/3rUypLMtrAcUc0Ykhx1Ix1T6UMCU6EShXJ0nMCqr2910NpnfYD50++ERmrb1DnvY4/+1IuoBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:15:39.381447Z"},"content_sha256":"d6f45fc57e4effc834d66e0c6032bb5ede1b53fae5bd248f8971d841d8935160","schema_version":"1.0","event_id":"sha256:d6f45fc57e4effc834d66e0c6032bb5ede1b53fae5bd248f8971d841d8935160"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GZKU3GLJNUBPIJJ2KDZ34IYVHI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Classification via Reference Distribution Learning: Theory and Practice","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jicong Fan, Zixiao Wang","submitted_at":"2024-08-21T06:42:22Z","abstract_excerpt":"Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kernels often suffer from computational costs and manual feature engineering, while GNNs commonly utilize global pooling operations, risking the loss of structural or semantic information. This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embedd"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11370","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/2408.11370/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":"2026-07-05T08:57:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y7bHuqoj1aL3FOmx2NwkaZQBsvPRptzMyl0xA+yyB0iDY6yU1Uc3f42KSfZMrkmlNBryFM40BgzUYTlg6LwPAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:15:39.381826Z"},"content_sha256":"05be94f11f127742dddba87f2d0d9e7f36e50ad056891339a5c16d4ac7f8c11d","schema_version":"1.0","event_id":"sha256:05be94f11f127742dddba87f2d0d9e7f36e50ad056891339a5c16d4ac7f8c11d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI/bundle.json","state_url":"https://pith.science/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI/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":"2026-07-07T07:15:39Z","links":{"resolver":"https://pith.science/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI","bundle":"https://pith.science/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI/bundle.json","state":"https://pith.science/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GZKU3GLJNUBPIJJ2KDZ34IYVHI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GZKU3GLJNUBPIJJ2KDZ34IYVHI","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":"43a1b0f07f2e20ff5a6392b52cad540984b476db133108d00d361ac230c98be5","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-21T06:42:22Z","title_canon_sha256":"860d252f39fdf7c54934c0ce89f52664aa65e07547087b63c09acdb7211ab76e"},"schema_version":"1.0","source":{"id":"2408.11370","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.11370","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"arxiv_version","alias_value":"2408.11370v1","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11370","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"pith_short_12","alias_value":"GZKU3GLJNUBP","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"pith_short_16","alias_value":"GZKU3GLJNUBPIJJ2","created_at":"2026-07-05T08:57:42Z"},{"alias_kind":"pith_short_8","alias_value":"GZKU3GLJ","created_at":"2026-07-05T08:57:42Z"}],"graph_snapshots":[{"event_id":"sha256:05be94f11f127742dddba87f2d0d9e7f36e50ad056891339a5c16d4ac7f8c11d","target":"graph","created_at":"2026-07-05T08:57:42Z","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/2408.11370/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kernels often suffer from computational costs and manual feature engineering, while GNNs commonly utilize global pooling operations, risking the loss of structural or semantic information. This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embedd","authors_text":"Jicong Fan, Zixiao Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-21T06:42:22Z","title":"Graph Classification via Reference Distribution Learning: Theory and Practice"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11370","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:d6f45fc57e4effc834d66e0c6032bb5ede1b53fae5bd248f8971d841d8935160","target":"record","created_at":"2026-07-05T08:57:42Z","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":"43a1b0f07f2e20ff5a6392b52cad540984b476db133108d00d361ac230c98be5","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-21T06:42:22Z","title_canon_sha256":"860d252f39fdf7c54934c0ce89f52664aa65e07547087b63c09acdb7211ab76e"},"schema_version":"1.0","source":{"id":"2408.11370","kind":"arxiv","version":1}},"canonical_sha256":"36554d99696d02f4253a50f3be23153a3f093c1b458b35b841164db4d305f9b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"36554d99696d02f4253a50f3be23153a3f093c1b458b35b841164db4d305f9b4","first_computed_at":"2026-07-05T08:57:42.793517Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:57:42.793517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7AZoiLovg/K5Rjn3LOIW7E9S6UZL8Q2ZVQWE1SHj6bsssRFvhywlTttO4xF+hTrxs4C7tW/pC8oOYSUT7VVWAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:57:42.793979Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.11370","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6f45fc57e4effc834d66e0c6032bb5ede1b53fae5bd248f8971d841d8935160","sha256:05be94f11f127742dddba87f2d0d9e7f36e50ad056891339a5c16d4ac7f8c11d"],"state_sha256":"5e0984485036eb2f20305f09a6935e007ef2b8d944783c7f61640df7351e5cbf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zx8rjW50KLwmJy2GdqY6KWCh8nIJEP4NRTUkfmiY+CEhFXxYYwAwedxbFSPE5Bx3BW9Im3Te0TWIYFiVsBkaAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:15:39.384253Z","bundle_sha256":"cdf0347ac4c31cc7628013d76fd58ab8421fc48d9a0dc050ea1f40fa36d2e799"}}