{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:PNVHSGCSCJB356RTTDYNJSWUGS","short_pith_number":"pith:PNVHSGCS","canonical_record":{"source":{"id":"2012.12533","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-23T08:10:19Z","cross_cats_sorted":[],"title_canon_sha256":"0469a3a9776b7d865351de1e151acbc8cb824080dbb3e20ee124a3be2d13b27f","abstract_canon_sha256":"763cb21973f4256e4b9a30ef4e3a512e888aa3800eb297c7ebad166a2eb68aea"},"schema_version":"1.0"},"canonical_sha256":"7b6a7918521243befa3398f0d4cad434a3a8dced7ca36aebcde00cfa220b8dc9","source":{"kind":"arxiv","id":"2012.12533","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.12533","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"arxiv_version","alias_value":"2012.12533v3","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.12533","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"pith_short_12","alias_value":"PNVHSGCSCJB3","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"pith_short_16","alias_value":"PNVHSGCSCJB356RT","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"pith_short_8","alias_value":"PNVHSGCS","created_at":"2026-07-05T02:32:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:PNVHSGCSCJB356RTTDYNJSWUGS","target":"record","payload":{"canonical_record":{"source":{"id":"2012.12533","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-23T08:10:19Z","cross_cats_sorted":[],"title_canon_sha256":"0469a3a9776b7d865351de1e151acbc8cb824080dbb3e20ee124a3be2d13b27f","abstract_canon_sha256":"763cb21973f4256e4b9a30ef4e3a512e888aa3800eb297c7ebad166a2eb68aea"},"schema_version":"1.0"},"canonical_sha256":"7b6a7918521243befa3398f0d4cad434a3a8dced7ca36aebcde00cfa220b8dc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:32:34.974667Z","signature_b64":"R2HDDSunFwZFoZqFczBzGEIwjI3IMWtlW+FngG7+x5hJoOAWWpV8JzAPSJeKDMg8QajJJDX6E2I6ck1yeug8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b6a7918521243befa3398f0d4cad434a3a8dced7ca36aebcde00cfa220b8dc9","last_reissued_at":"2026-07-05T02:32:34.974187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:32:34.974187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.12533","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":"2026-07-05T02:32:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n8UpXFIh4BT4RLO7UbFW0sPBV1x7QKQ64R0EywfdhVhttZ8/yvRampsYNXfmJzmBSVNMrGRQSzW4TB9Xh7NuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T21:42:04.658054Z"},"content_sha256":"b0935b45a948886ce8e3ce5aec8e83318ad1b4e54f1660493f009b82bd935896","schema_version":"1.0","event_id":"sha256:b0935b45a948886ce8e3ce5aec8e83318ad1b4e54f1660493f009b82bd935896"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:PNVHSGCSCJB356RTTDYNJSWUGS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Motif-Driven Contrastive Learning of Graph Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arjun Subramonian, Shichang Zhang, Yizhou Sun, Ziniu Hu","submitted_at":"2020-12-23T08:10:19Z","abstract_excerpt":"Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global graph structure. The key challenge to conducting subgraph-level contrastive learning is to sample informative subgraphs that are semantically meaningful. To solve it, we propose to learn graph motifs, which are frequently-occurring subgraph patterns (e.g. functional groups of molecules), for better subgraph sampling. Our framework MotIf-driven Contrastive leaRning Of Graph repre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.12533","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/2012.12533/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":"2026-07-05T02:32:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uGP8MM1bTRlDqryIkW+Y3Y5KPJQNjmpKUGXybKZtuX4jNA7DRl1LJyi6XeEDCpA6UWP59vZYzJjhRBUufIQcAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T21:42:04.658646Z"},"content_sha256":"86b4f64b424935adbae64c11d27f5919978e831a7fe8dc005a66dbfd61c9af7e","schema_version":"1.0","event_id":"sha256:86b4f64b424935adbae64c11d27f5919978e831a7fe8dc005a66dbfd61c9af7e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PNVHSGCSCJB356RTTDYNJSWUGS/bundle.json","state_url":"https://pith.science/pith/PNVHSGCSCJB356RTTDYNJSWUGS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PNVHSGCSCJB356RTTDYNJSWUGS/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":"2026-08-20T21:42:04Z","links":{"resolver":"https://pith.science/pith/PNVHSGCSCJB356RTTDYNJSWUGS","bundle":"https://pith.science/pith/PNVHSGCSCJB356RTTDYNJSWUGS/bundle.json","state":"https://pith.science/pith/PNVHSGCSCJB356RTTDYNJSWUGS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PNVHSGCSCJB356RTTDYNJSWUGS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:PNVHSGCSCJB356RTTDYNJSWUGS","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":"763cb21973f4256e4b9a30ef4e3a512e888aa3800eb297c7ebad166a2eb68aea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-23T08:10:19Z","title_canon_sha256":"0469a3a9776b7d865351de1e151acbc8cb824080dbb3e20ee124a3be2d13b27f"},"schema_version":"1.0","source":{"id":"2012.12533","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.12533","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"arxiv_version","alias_value":"2012.12533v3","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.12533","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"pith_short_12","alias_value":"PNVHSGCSCJB3","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"pith_short_16","alias_value":"PNVHSGCSCJB356RT","created_at":"2026-07-05T02:32:34Z"},{"alias_kind":"pith_short_8","alias_value":"PNVHSGCS","created_at":"2026-07-05T02:32:34Z"}],"graph_snapshots":[{"event_id":"sha256:86b4f64b424935adbae64c11d27f5919978e831a7fe8dc005a66dbfd61c9af7e","target":"graph","created_at":"2026-07-05T02:32:34Z","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/2012.12533/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global graph structure. The key challenge to conducting subgraph-level contrastive learning is to sample informative subgraphs that are semantically meaningful. To solve it, we propose to learn graph motifs, which are frequently-occurring subgraph patterns (e.g. functional groups of molecules), for better subgraph sampling. Our framework MotIf-driven Contrastive leaRning Of Graph repre","authors_text":"Arjun Subramonian, Shichang Zhang, Yizhou Sun, Ziniu Hu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-23T08:10:19Z","title":"Motif-Driven Contrastive Learning of Graph Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.12533","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:b0935b45a948886ce8e3ce5aec8e83318ad1b4e54f1660493f009b82bd935896","target":"record","created_at":"2026-07-05T02:32:34Z","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":"763cb21973f4256e4b9a30ef4e3a512e888aa3800eb297c7ebad166a2eb68aea","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-23T08:10:19Z","title_canon_sha256":"0469a3a9776b7d865351de1e151acbc8cb824080dbb3e20ee124a3be2d13b27f"},"schema_version":"1.0","source":{"id":"2012.12533","kind":"arxiv","version":3}},"canonical_sha256":"7b6a7918521243befa3398f0d4cad434a3a8dced7ca36aebcde00cfa220b8dc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7b6a7918521243befa3398f0d4cad434a3a8dced7ca36aebcde00cfa220b8dc9","first_computed_at":"2026-07-05T02:32:34.974187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:32:34.974187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R2HDDSunFwZFoZqFczBzGEIwjI3IMWtlW+FngG7+x5hJoOAWWpV8JzAPSJeKDMg8QajJJDX6E2I6ck1yeug8DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:32:34.974667Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.12533","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0935b45a948886ce8e3ce5aec8e83318ad1b4e54f1660493f009b82bd935896","sha256:86b4f64b424935adbae64c11d27f5919978e831a7fe8dc005a66dbfd61c9af7e"],"state_sha256":"d2d0e5bce0e0c986994bddec672407bd0b0afa2c0129a9d3f590d392c212eda3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1hAC0fye8sEzzbJISpd5aIt3dMYLiZ2TryecHNyDRm2WvbzjcMzfGtjdCsCRIi537dERV0sdjTbO0zFaEpLvDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T21:42:04.663406Z","bundle_sha256":"b4e9bb761b1f2fd8cdc3e559927f9bb81d563772f6e49bc81973ddb3157d2d4e"}}