{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:H5BYUOLUB2CKW6EQES3S3H22NM","short_pith_number":"pith:H5BYUOLU","canonical_record":{"source":{"id":"2606.10284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T01:13:44Z","cross_cats_sorted":[],"title_canon_sha256":"68eed4e8f5744aadfd005a6b9b9ae3666538614403779942209c78b558fdcc93","abstract_canon_sha256":"8a58d6fd5730389f1c75ce51f017c6f4e20341c528b6ecf0abcdc56b6f4a2c86"},"schema_version":"1.0"},"canonical_sha256":"3f438a39740e84ab789024b72d9f5a6b201dc73fa48dd9502c46858b56bfc4dc","source":{"kind":"arxiv","id":"2606.10284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10284","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10284v1","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10284","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"pith_short_12","alias_value":"H5BYUOLUB2CK","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"pith_short_16","alias_value":"H5BYUOLUB2CKW6EQ","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"pith_short_8","alias_value":"H5BYUOLU","created_at":"2026-06-10T01:10:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:H5BYUOLUB2CKW6EQES3S3H22NM","target":"record","payload":{"canonical_record":{"source":{"id":"2606.10284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T01:13:44Z","cross_cats_sorted":[],"title_canon_sha256":"68eed4e8f5744aadfd005a6b9b9ae3666538614403779942209c78b558fdcc93","abstract_canon_sha256":"8a58d6fd5730389f1c75ce51f017c6f4e20341c528b6ecf0abcdc56b6f4a2c86"},"schema_version":"1.0"},"canonical_sha256":"3f438a39740e84ab789024b72d9f5a6b201dc73fa48dd9502c46858b56bfc4dc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:08.868488Z","signature_b64":"5aWq9i8pGBqMrtLo4qfKamnPLR8+Odmm5qWnMl12TJ0LujZUy2d9zCUq25QfZFcdgQeFCB38yL4iy4XiYeABAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f438a39740e84ab789024b72d9f5a6b201dc73fa48dd9502c46858b56bfc4dc","last_reissued_at":"2026-06-10T01:10:08.867662Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:08.867662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.10284","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":"2026-06-10T01:10:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MlKinlGXrI4x4vYPNhUIuFglxm5AhsbdOECLJowST+t6MET7iulbcsi20HPsPDiUfNi3yyt+j+z05smfifb4Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T09:59:30.337739Z"},"content_sha256":"5f46cf22cb61f497352af39967a010d4e74ccf7c71ff901a8a7cb9d72af4855f","schema_version":"1.0","event_id":"sha256:5f46cf22cb61f497352af39967a010d4e74ccf7c71ff901a8a7cb9d72af4855f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:H5BYUOLUB2CKW6EQES3S3H22NM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Di Jin, Dongxiao He, Jitao Zhao, Lianze Shan, Ningchong Wang","submitted_at":"2026-06-09T01:13:44Z","abstract_excerpt":"Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm. It is widely recognized that positive samples are essential in GCLs. Ideally, maximizing the similarity of positive samples enables graph encoders to capture intrinsic semantic and patterns of graph data. However, we discover an interesting phenomenon: GCLs can achieve competitive performance even without positive samples. This motivates us to revisit the fundamental mechanism of positiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10284","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/2606.10284/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":"2026-06-10T01:10:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XchewdZ1Glj88EXz0Rpfao1LjT7fgbVYOZn5Q6E/Cr1cG/5B4mGb84Wfvq1wH77tsI82rUNMUiMCiRKDT9jlDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T09:59:30.338111Z"},"content_sha256":"7af9a55551d71095c03603e043c7aea930d4b600c9855660f3d3ca42ad03973b","schema_version":"1.0","event_id":"sha256:7af9a55551d71095c03603e043c7aea930d4b600c9855660f3d3ca42ad03973b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H5BYUOLUB2CKW6EQES3S3H22NM/bundle.json","state_url":"https://pith.science/pith/H5BYUOLUB2CKW6EQES3S3H22NM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H5BYUOLUB2CKW6EQES3S3H22NM/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":"2026-07-03T09:59:30Z","links":{"resolver":"https://pith.science/pith/H5BYUOLUB2CKW6EQES3S3H22NM","bundle":"https://pith.science/pith/H5BYUOLUB2CKW6EQES3S3H22NM/bundle.json","state":"https://pith.science/pith/H5BYUOLUB2CKW6EQES3S3H22NM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H5BYUOLUB2CKW6EQES3S3H22NM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:H5BYUOLUB2CKW6EQES3S3H22NM","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":"8a58d6fd5730389f1c75ce51f017c6f4e20341c528b6ecf0abcdc56b6f4a2c86","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T01:13:44Z","title_canon_sha256":"68eed4e8f5744aadfd005a6b9b9ae3666538614403779942209c78b558fdcc93"},"schema_version":"1.0","source":{"id":"2606.10284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10284","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10284v1","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10284","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"pith_short_12","alias_value":"H5BYUOLUB2CK","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"pith_short_16","alias_value":"H5BYUOLUB2CKW6EQ","created_at":"2026-06-10T01:10:08Z"},{"alias_kind":"pith_short_8","alias_value":"H5BYUOLU","created_at":"2026-06-10T01:10:08Z"}],"graph_snapshots":[{"event_id":"sha256:7af9a55551d71095c03603e043c7aea930d4b600c9855660f3d3ca42ad03973b","target":"graph","created_at":"2026-06-10T01:10:08Z","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/2606.10284/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm. It is widely recognized that positive samples are essential in GCLs. Ideally, maximizing the similarity of positive samples enables graph encoders to capture intrinsic semantic and patterns of graph data. However, we discover an interesting phenomenon: GCLs can achieve competitive performance even without positive samples. This motivates us to revisit the fundamental mechanism of positiv","authors_text":"Di Jin, Dongxiao He, Jitao Zhao, Lianze Shan, Ningchong Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T01:13:44Z","title":"Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10284","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":"sha256:5f46cf22cb61f497352af39967a010d4e74ccf7c71ff901a8a7cb9d72af4855f","target":"record","created_at":"2026-06-10T01:10:08Z","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":"8a58d6fd5730389f1c75ce51f017c6f4e20341c528b6ecf0abcdc56b6f4a2c86","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T01:13:44Z","title_canon_sha256":"68eed4e8f5744aadfd005a6b9b9ae3666538614403779942209c78b558fdcc93"},"schema_version":"1.0","source":{"id":"2606.10284","kind":"arxiv","version":1}},"canonical_sha256":"3f438a39740e84ab789024b72d9f5a6b201dc73fa48dd9502c46858b56bfc4dc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f438a39740e84ab789024b72d9f5a6b201dc73fa48dd9502c46858b56bfc4dc","first_computed_at":"2026-06-10T01:10:08.867662Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T01:10:08.867662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5aWq9i8pGBqMrtLo4qfKamnPLR8+Odmm5qWnMl12TJ0LujZUy2d9zCUq25QfZFcdgQeFCB38yL4iy4XiYeABAw==","signature_status":"signed_v1","signed_at":"2026-06-10T01:10:08.868488Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.10284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5f46cf22cb61f497352af39967a010d4e74ccf7c71ff901a8a7cb9d72af4855f","sha256:7af9a55551d71095c03603e043c7aea930d4b600c9855660f3d3ca42ad03973b"],"state_sha256":"3faee7ed781a19e40feb43e232d347990ef53cc64dbbb11f3cca8a764662b981"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qRJarixpWZfOf6Kt8omrkCzp5yFh54cWVuz44DR7dYBJA3w/f25jNBKvj/fzsp49Yvk6x5Mkh/EJyd6qRPVBBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T09:59:30.340031Z","bundle_sha256":"f652b4dabbeb2926c405cc78c510b3d220074367bc97dfe32e335a4c81098ebd"}}