{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:24SC5C7DTB7VVY4U56KBYQJTFA","short_pith_number":"pith:24SC5C7D","canonical_record":{"source":{"id":"2406.10426","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-14T22:07:11Z","cross_cats_sorted":[],"title_canon_sha256":"55a867d8256d854099a8437387b0437475480a3214b61e1af1dd05001eb1fd8e","abstract_canon_sha256":"b4e161975562552280bd1da126f6a6740b6f32d1daf04a62cdfae6473d411958"},"schema_version":"1.0"},"canonical_sha256":"d7242e8be3987f5ae394ef941c4133281a39c0f5a2e75d72c2faac975daaa8ef","source":{"kind":"arxiv","id":"2406.10426","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.10426","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"arxiv_version","alias_value":"2406.10426v3","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10426","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"pith_short_12","alias_value":"24SC5C7DTB7V","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"pith_short_16","alias_value":"24SC5C7DTB7VVY4U","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"pith_short_8","alias_value":"24SC5C7D","created_at":"2026-07-05T10:14:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:24SC5C7DTB7VVY4U56KBYQJTFA","target":"record","payload":{"canonical_record":{"source":{"id":"2406.10426","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-14T22:07:11Z","cross_cats_sorted":[],"title_canon_sha256":"55a867d8256d854099a8437387b0437475480a3214b61e1af1dd05001eb1fd8e","abstract_canon_sha256":"b4e161975562552280bd1da126f6a6740b6f32d1daf04a62cdfae6473d411958"},"schema_version":"1.0"},"canonical_sha256":"d7242e8be3987f5ae394ef941c4133281a39c0f5a2e75d72c2faac975daaa8ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:41.702267Z","signature_b64":"lHhqsj91DWgS3it7ytfNeuY1XP7yAD8qc0po6pK43vMNF6eTLiKUeu/kZdrb9mJhMQdBQGCtHfL+m0woUQeFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7242e8be3987f5ae394ef941c4133281a39c0f5a2e75d72c2faac975daaa8ef","last_reissued_at":"2026-07-05T10:14:41.701754Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:41.701754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.10426","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":"2026-07-05T10:14:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vGjpPZIyYh0J9j35d164Oq3QWLc9dQ69S9qV835RDX+Juz5uwY/rHvfA2n5BgKxGiB5Dlkkk+41zg581ns1bBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:57:32.045966Z"},"content_sha256":"8134ad8b0789595812b0b51eaebc505ee6d0c8fc6e29aa1ce304fdd9cbdf31c5","schema_version":"1.0","event_id":"sha256:8134ad8b0789595812b0b51eaebc505ee6d0c8fc6e29aa1ce304fdd9cbdf31c5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:24SC5C7DTB7VVY4U56KBYQJTFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Baris Coskunuzer, Cuneyt Gurcan Akcora, Farimah Poursafaei, Guillaume Rabusseau, Kiarash Shamsi, Poupak Azad, Razieh Shirzadkhani, Reihaneh Rabbany, Shenyang Huang, Tran Gia Bao Ngo","submitted_at":"2024-06-14T22:07:11Z","abstract_excerpt":"Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely concentrated on learning from individual networks, this study explores the potential of learning from multiple temporal networks and its ability to transfer to unobserved networks. To achieve this, we introduce Temporal Multi-network Training MiNT, a novel pre-training approach that learns from multiple temporal networks. With a novel collection of 84 temporal transaction networks, we pre-train TGL models on up to 64 ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10426","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/2406.10426/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":"2026-07-05T10:14:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cJxrbd+33tydBjVD6cLzkBvyHskHTvHGljC9k/Ox1qHBiFuowf4FeYXUsYORVn07sORw3cfIA8CgggeMmhgVAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:57:32.046503Z"},"content_sha256":"6cccb4a412853abed3fc2a8afbff6a7dfb87622a10e90ce641c827be07a375a7","schema_version":"1.0","event_id":"sha256:6cccb4a412853abed3fc2a8afbff6a7dfb87622a10e90ce641c827be07a375a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/24SC5C7DTB7VVY4U56KBYQJTFA/bundle.json","state_url":"https://pith.science/pith/24SC5C7DTB7VVY4U56KBYQJTFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/24SC5C7DTB7VVY4U56KBYQJTFA/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":"2026-08-08T22:57:32Z","links":{"resolver":"https://pith.science/pith/24SC5C7DTB7VVY4U56KBYQJTFA","bundle":"https://pith.science/pith/24SC5C7DTB7VVY4U56KBYQJTFA/bundle.json","state":"https://pith.science/pith/24SC5C7DTB7VVY4U56KBYQJTFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/24SC5C7DTB7VVY4U56KBYQJTFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:24SC5C7DTB7VVY4U56KBYQJTFA","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":"b4e161975562552280bd1da126f6a6740b6f32d1daf04a62cdfae6473d411958","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-14T22:07:11Z","title_canon_sha256":"55a867d8256d854099a8437387b0437475480a3214b61e1af1dd05001eb1fd8e"},"schema_version":"1.0","source":{"id":"2406.10426","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.10426","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"arxiv_version","alias_value":"2406.10426v3","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10426","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"pith_short_12","alias_value":"24SC5C7DTB7V","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"pith_short_16","alias_value":"24SC5C7DTB7VVY4U","created_at":"2026-07-05T10:14:41Z"},{"alias_kind":"pith_short_8","alias_value":"24SC5C7D","created_at":"2026-07-05T10:14:41Z"}],"graph_snapshots":[{"event_id":"sha256:6cccb4a412853abed3fc2a8afbff6a7dfb87622a10e90ce641c827be07a375a7","target":"graph","created_at":"2026-07-05T10:14:41Z","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/2406.10426/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely concentrated on learning from individual networks, this study explores the potential of learning from multiple temporal networks and its ability to transfer to unobserved networks. To achieve this, we introduce Temporal Multi-network Training MiNT, a novel pre-training approach that learns from multiple temporal networks. With a novel collection of 84 temporal transaction networks, we pre-train TGL models on up to 64 ne","authors_text":"Baris Coskunuzer, Cuneyt Gurcan Akcora, Farimah Poursafaei, Guillaume Rabusseau, Kiarash Shamsi, Poupak Azad, Razieh Shirzadkhani, Reihaneh Rabbany, Shenyang Huang, Tran Gia Bao Ngo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-14T22:07:11Z","title":"MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10426","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":"sha256:8134ad8b0789595812b0b51eaebc505ee6d0c8fc6e29aa1ce304fdd9cbdf31c5","target":"record","created_at":"2026-07-05T10:14:41Z","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":"b4e161975562552280bd1da126f6a6740b6f32d1daf04a62cdfae6473d411958","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-14T22:07:11Z","title_canon_sha256":"55a867d8256d854099a8437387b0437475480a3214b61e1af1dd05001eb1fd8e"},"schema_version":"1.0","source":{"id":"2406.10426","kind":"arxiv","version":3}},"canonical_sha256":"d7242e8be3987f5ae394ef941c4133281a39c0f5a2e75d72c2faac975daaa8ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7242e8be3987f5ae394ef941c4133281a39c0f5a2e75d72c2faac975daaa8ef","first_computed_at":"2026-07-05T10:14:41.701754Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:14:41.701754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lHhqsj91DWgS3it7ytfNeuY1XP7yAD8qc0po6pK43vMNF6eTLiKUeu/kZdrb9mJhMQdBQGCtHfL+m0woUQeFAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:14:41.702267Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.10426","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8134ad8b0789595812b0b51eaebc505ee6d0c8fc6e29aa1ce304fdd9cbdf31c5","sha256:6cccb4a412853abed3fc2a8afbff6a7dfb87622a10e90ce641c827be07a375a7"],"state_sha256":"da209e395bfc04748f85d921498a56d88665e39684a3977c8e76dc533557b6f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XCEYRGR0AnxMKi404JyZGkjTXQLN4V7izWxJO8c7cYkJQnZFEkq4X4EDqNj6USRBteniEI50dNmdOiDgoF4ACA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:57:32.052623Z","bundle_sha256":"780fbcbe33304dd17ecf3fbfab2a08020de7230c90409db0f9f99eb2849102e2"}}