{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EATNNI7I22UADDZXIEY6TUUQJC","short_pith_number":"pith:EATNNI7I","canonical_record":{"source":{"id":"2006.13463","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-24T04:07:25Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"753a16f631b3ece1c4835261544697ca3eab4adfabe82334e0ea1f56b44a41ca","abstract_canon_sha256":"45fa2a6a877de76e0b160d79d939e78f821bdba8a4c7711b64b1858fcfe804d5"},"schema_version":"1.0"},"canonical_sha256":"2026d6a3e8d6a8018f374131e9d29048bf1192be85a9e813d7248bf6a77b3069","source":{"kind":"arxiv","id":"2006.13463","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.13463","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"arxiv_version","alias_value":"2006.13463v2","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.13463","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"pith_short_12","alias_value":"EATNNI7I22UA","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"pith_short_16","alias_value":"EATNNI7I22UADDZX","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"pith_short_8","alias_value":"EATNNI7I","created_at":"2026-07-05T01:45:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EATNNI7I22UADDZXIEY6TUUQJC","target":"record","payload":{"canonical_record":{"source":{"id":"2006.13463","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-24T04:07:25Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"753a16f631b3ece1c4835261544697ca3eab4adfabe82334e0ea1f56b44a41ca","abstract_canon_sha256":"45fa2a6a877de76e0b160d79d939e78f821bdba8a4c7711b64b1858fcfe804d5"},"schema_version":"1.0"},"canonical_sha256":"2026d6a3e8d6a8018f374131e9d29048bf1192be85a9e813d7248bf6a77b3069","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:28.778922Z","signature_b64":"Zz4lIeYhBLTZTlRtUn8xueMtkPCvMTA5UYfcP+TJe9KFhFsfGt6p+5YIyOP/jY+z1SWrLEPw+oxOuCx2imI/BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2026d6a3e8d6a8018f374131e9d29048bf1192be85a9e813d7248bf6a77b3069","last_reissued_at":"2026-07-05T01:45:28.778406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:28.778406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.13463","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:45:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FN9dwSH/aAwbP8FXpgMbdCfijE/Vh0Pg+PUokrEjgETsQjgsXTM19x7Uo+XSMfLODqqrMMzylgDZj8zCSQoaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:23:23.325177Z"},"content_sha256":"60cfe4693345b50b1145d59ee96e34730118965394ecac70a61fb468b99cb5d8","schema_version":"1.0","event_id":"sha256:60cfe4693345b50b1145d59ee96e34730118965394ecac70a61fb468b99cb5d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EATNNI7I22UADDZXIEY6TUUQJC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Policy Network for Transferable Active Learning on Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jian Tang, Marc-Alexandre C\\^ot\\'e, Meng Qu, Shengding Hu, Xingdi Yuan, Zheng Xiong, Zhiyuan Liu","submitted_at":"2020-06-24T04:07:25Z","abstract_excerpt":"Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is generally required to train these networks, which could be very expensive to obtain in some domains. In this paper, we study active learning for GNNs, i.e., how to efficiently label the nodes on a graph to reduce the annotation cost of training GNNs. We formulate the problem as a sequential decision process on graphs and train a GNN-based policy network with reinforcement learning to learn the optimal query strategy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.13463","kind":"arxiv","version":2},"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/2006.13463/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":"2026-07-05T01:45:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eUDCDywSIr+rtqwU9xHFOw7+3p60NpuQa6ym0uNz2UgFLSgtOH5+q5twsZQUqAllUMr2QzW+78lsfYzbcF21AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:23:23.325684Z"},"content_sha256":"12602a0744b6e39660d5e653ca92f83de0f97a7cbaf55e0452ab52ccde7e8a1e","schema_version":"1.0","event_id":"sha256:12602a0744b6e39660d5e653ca92f83de0f97a7cbaf55e0452ab52ccde7e8a1e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EATNNI7I22UADDZXIEY6TUUQJC/bundle.json","state_url":"https://pith.science/pith/EATNNI7I22UADDZXIEY6TUUQJC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EATNNI7I22UADDZXIEY6TUUQJC/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":"2026-08-03T18:23:23Z","links":{"resolver":"https://pith.science/pith/EATNNI7I22UADDZXIEY6TUUQJC","bundle":"https://pith.science/pith/EATNNI7I22UADDZXIEY6TUUQJC/bundle.json","state":"https://pith.science/pith/EATNNI7I22UADDZXIEY6TUUQJC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EATNNI7I22UADDZXIEY6TUUQJC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EATNNI7I22UADDZXIEY6TUUQJC","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":"45fa2a6a877de76e0b160d79d939e78f821bdba8a4c7711b64b1858fcfe804d5","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-24T04:07:25Z","title_canon_sha256":"753a16f631b3ece1c4835261544697ca3eab4adfabe82334e0ea1f56b44a41ca"},"schema_version":"1.0","source":{"id":"2006.13463","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.13463","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"arxiv_version","alias_value":"2006.13463v2","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.13463","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"pith_short_12","alias_value":"EATNNI7I22UA","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"pith_short_16","alias_value":"EATNNI7I22UADDZX","created_at":"2026-07-05T01:45:28Z"},{"alias_kind":"pith_short_8","alias_value":"EATNNI7I","created_at":"2026-07-05T01:45:28Z"}],"graph_snapshots":[{"event_id":"sha256:12602a0744b6e39660d5e653ca92f83de0f97a7cbaf55e0452ab52ccde7e8a1e","target":"graph","created_at":"2026-07-05T01:45:28Z","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/2006.13463/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is generally required to train these networks, which could be very expensive to obtain in some domains. In this paper, we study active learning for GNNs, i.e., how to efficiently label the nodes on a graph to reduce the annotation cost of training GNNs. We formulate the problem as a sequential decision process on graphs and train a GNN-based policy network with reinforcement learning to learn the optimal query strategy","authors_text":"Jian Tang, Marc-Alexandre C\\^ot\\'e, Meng Qu, Shengding Hu, Xingdi Yuan, Zheng Xiong, Zhiyuan Liu","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-24T04:07:25Z","title":"Graph Policy Network for Transferable Active Learning on Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.13463","kind":"arxiv","version":2},"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":"sha256:60cfe4693345b50b1145d59ee96e34730118965394ecac70a61fb468b99cb5d8","target":"record","created_at":"2026-07-05T01:45:28Z","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":"45fa2a6a877de76e0b160d79d939e78f821bdba8a4c7711b64b1858fcfe804d5","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-24T04:07:25Z","title_canon_sha256":"753a16f631b3ece1c4835261544697ca3eab4adfabe82334e0ea1f56b44a41ca"},"schema_version":"1.0","source":{"id":"2006.13463","kind":"arxiv","version":2}},"canonical_sha256":"2026d6a3e8d6a8018f374131e9d29048bf1192be85a9e813d7248bf6a77b3069","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2026d6a3e8d6a8018f374131e9d29048bf1192be85a9e813d7248bf6a77b3069","first_computed_at":"2026-07-05T01:45:28.778406Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:45:28.778406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zz4lIeYhBLTZTlRtUn8xueMtkPCvMTA5UYfcP+TJe9KFhFsfGt6p+5YIyOP/jY+z1SWrLEPw+oxOuCx2imI/BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:45:28.778922Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.13463","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60cfe4693345b50b1145d59ee96e34730118965394ecac70a61fb468b99cb5d8","sha256:12602a0744b6e39660d5e653ca92f83de0f97a7cbaf55e0452ab52ccde7e8a1e"],"state_sha256":"e1b4d3583db4f8fc5fe4b869ffb87431cc4bb2728502bafe9c0bc32485f9b951"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q6dNBK2DHwjbQRFIhiRpBMUJOvVG6Wv3GvZmvZBj+kPy2laVG+rBpfRP8Q+T5sHY8bDr2ZyaWXWFftI6tTSwDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:23:23.331131Z","bundle_sha256":"811ce3edaf3734c312b14df5b1d2b650f1415b0a19c3377b6f830e145bb536c6"}}