{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2TG4UBOWZJTGWXGBCIMYRJA3KH","short_pith_number":"pith:2TG4UBOW","canonical_record":{"source":{"id":"2404.19109","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-29T21:19:41Z","cross_cats_sorted":["q-fin.GN"],"title_canon_sha256":"d01f9d2c9f0e1234d84879baefd2583cf0ab9af3adf1d983ebcf74b7e346edee","abstract_canon_sha256":"232d820729899d1f455a96f5b23c80de1c8a8c7f2391956df1c2ee790db74af1"},"schema_version":"1.0"},"canonical_sha256":"d4cdca05d6ca666b5cc1121988a41b51dc107542cb357018f518b3d3888e306e","source":{"kind":"arxiv","id":"2404.19109","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.19109","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"arxiv_version","alias_value":"2404.19109v3","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.19109","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"pith_short_12","alias_value":"2TG4UBOWZJTG","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"pith_short_16","alias_value":"2TG4UBOWZJTGWXGB","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"pith_short_8","alias_value":"2TG4UBOW","created_at":"2026-07-05T08:49:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2TG4UBOWZJTGWXGBCIMYRJA3KH","target":"record","payload":{"canonical_record":{"source":{"id":"2404.19109","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-29T21:19:41Z","cross_cats_sorted":["q-fin.GN"],"title_canon_sha256":"d01f9d2c9f0e1234d84879baefd2583cf0ab9af3adf1d983ebcf74b7e346edee","abstract_canon_sha256":"232d820729899d1f455a96f5b23c80de1c8a8c7f2391956df1c2ee790db74af1"},"schema_version":"1.0"},"canonical_sha256":"d4cdca05d6ca666b5cc1121988a41b51dc107542cb357018f518b3d3888e306e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:33.751999Z","signature_b64":"8yXu6+o9WCwd04M2rKxtiPPqw0JWxDcPOIjkxVnrKrtvheFjI4oslSky7ER9gZO4LYJxQ09bAVftR9rC3VItDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4cdca05d6ca666b5cc1121988a41b51dc107542cb357018f518b3d3888e306e","last_reissued_at":"2026-07-05T08:49:33.751426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:33.751426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.19109","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:49:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a40FB3euY6VvHBK8Mz8E0/GSMW7Y/8BwjeIP/QSxTjZQkZeyEa2S6BCE4C+7R+uxENH97DTJ2MbZJqrVBjMjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:35.864940Z"},"content_sha256":"c05a7a2a0ec991c77681fcb0e072167af74c5238ff446e2088a7b17ceca5c722","schema_version":"1.0","event_id":"sha256:c05a7a2a0ec991c77681fcb0e072167af74c5238ff446e2088a7b17ceca5c722"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2TG4UBOWZJTGWXGBCIMYRJA3KH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-fin.GN"],"primary_cat":"cs.LG","authors_text":"Arvind, Charles E. Leiserson, Claudio Bellei, Jie Chen, Mark Weber, Muhua Xu, Ross Phillips, Tim Kaler, Tom Robinson","submitted_at":"2024-04-29T21:19:41Z","abstract_excerpt":"Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.19109","kind":"arxiv","version":3},"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/2404.19109/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":"2026-07-05T08:49:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wwy31rXy7AoeldyxUXesj2vAha6+8a/beTT2ytdvR2B+RI2OD9oxZFST6dKalo94bCZhackVbzNYrWQGiRl6AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:35.865957Z"},"content_sha256":"2ef1fdcfd499023993019f1e87fbeeff97b8f42f8a3a6077b21c01e90f9c70bb","schema_version":"1.0","event_id":"sha256:2ef1fdcfd499023993019f1e87fbeeff97b8f42f8a3a6077b21c01e90f9c70bb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH/bundle.json","state_url":"https://pith.science/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH/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":"2026-08-05T03:03:35Z","links":{"resolver":"https://pith.science/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH","bundle":"https://pith.science/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH/bundle.json","state":"https://pith.science/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2TG4UBOWZJTGWXGBCIMYRJA3KH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2TG4UBOWZJTGWXGBCIMYRJA3KH","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":"232d820729899d1f455a96f5b23c80de1c8a8c7f2391956df1c2ee790db74af1","cross_cats_sorted":["q-fin.GN"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-29T21:19:41Z","title_canon_sha256":"d01f9d2c9f0e1234d84879baefd2583cf0ab9af3adf1d983ebcf74b7e346edee"},"schema_version":"1.0","source":{"id":"2404.19109","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.19109","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"arxiv_version","alias_value":"2404.19109v3","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.19109","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"pith_short_12","alias_value":"2TG4UBOWZJTG","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"pith_short_16","alias_value":"2TG4UBOWZJTGWXGB","created_at":"2026-07-05T08:49:33Z"},{"alias_kind":"pith_short_8","alias_value":"2TG4UBOW","created_at":"2026-07-05T08:49:33Z"}],"graph_snapshots":[{"event_id":"sha256:2ef1fdcfd499023993019f1e87fbeeff97b8f42f8a3a6077b21c01e90f9c70bb","target":"graph","created_at":"2026-07-05T08:49:33Z","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/2404.19109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world ","authors_text":"Arvind, Charles E. Leiserson, Claudio Bellei, Jie Chen, Mark Weber, Muhua Xu, Ross Phillips, Tim Kaler, Tom Robinson","cross_cats":["q-fin.GN"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-29T21:19:41Z","title":"The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.19109","kind":"arxiv","version":3},"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:c05a7a2a0ec991c77681fcb0e072167af74c5238ff446e2088a7b17ceca5c722","target":"record","created_at":"2026-07-05T08:49:33Z","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":"232d820729899d1f455a96f5b23c80de1c8a8c7f2391956df1c2ee790db74af1","cross_cats_sorted":["q-fin.GN"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-29T21:19:41Z","title_canon_sha256":"d01f9d2c9f0e1234d84879baefd2583cf0ab9af3adf1d983ebcf74b7e346edee"},"schema_version":"1.0","source":{"id":"2404.19109","kind":"arxiv","version":3}},"canonical_sha256":"d4cdca05d6ca666b5cc1121988a41b51dc107542cb357018f518b3d3888e306e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d4cdca05d6ca666b5cc1121988a41b51dc107542cb357018f518b3d3888e306e","first_computed_at":"2026-07-05T08:49:33.751426Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:49:33.751426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8yXu6+o9WCwd04M2rKxtiPPqw0JWxDcPOIjkxVnrKrtvheFjI4oslSky7ER9gZO4LYJxQ09bAVftR9rC3VItDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:49:33.751999Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.19109","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c05a7a2a0ec991c77681fcb0e072167af74c5238ff446e2088a7b17ceca5c722","sha256:2ef1fdcfd499023993019f1e87fbeeff97b8f42f8a3a6077b21c01e90f9c70bb"],"state_sha256":"0dd27337d534aa8fcb8c4c21b6102db0c146b6d99b7410941708b797e55f3eca"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1ZnTZs2lVwwbB5aU49oLEKtJLz4ChBPmKdot92VZ51v9Wl2uv/5/PE+QYCrjl4pEdpv+d6hPJPqzVdgXw//6Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:03:35.872652Z","bundle_sha256":"83804d909a03c3b431aea7246baa45820c37364354a618f33312f65e9342cf2e"}}