{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YFNKV2TJHY4Z6ZNSXVRHQJMZUR","short_pith_number":"pith:YFNKV2TJ","canonical_record":{"source":{"id":"2505.15103","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T04:54:18Z","cross_cats_sorted":[],"title_canon_sha256":"ade8dccb89b2548c787dbfc2563aa8b7427e273970586fe23a40b906e4e0dc12","abstract_canon_sha256":"0108c6d11110a3e531c5b1fd75f9777f14ade69d91f6766903dae1ddf2a722f8"},"schema_version":"1.0"},"canonical_sha256":"c15aaaea693e399f65b2bd62782599a46d9906c17091a2d539bbf439492fd923","source":{"kind":"arxiv","id":"2505.15103","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15103","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15103v2","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15103","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"pith_short_12","alias_value":"YFNKV2TJHY4Z","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"pith_short_16","alias_value":"YFNKV2TJHY4Z6ZNS","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"pith_short_8","alias_value":"YFNKV2TJ","created_at":"2026-07-05T11:50:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YFNKV2TJHY4Z6ZNSXVRHQJMZUR","target":"record","payload":{"canonical_record":{"source":{"id":"2505.15103","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T04:54:18Z","cross_cats_sorted":[],"title_canon_sha256":"ade8dccb89b2548c787dbfc2563aa8b7427e273970586fe23a40b906e4e0dc12","abstract_canon_sha256":"0108c6d11110a3e531c5b1fd75f9777f14ade69d91f6766903dae1ddf2a722f8"},"schema_version":"1.0"},"canonical_sha256":"c15aaaea693e399f65b2bd62782599a46d9906c17091a2d539bbf439492fd923","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:36.854749Z","signature_b64":"pJf332frCxhG5obnx4Ai18O9n3iBWUox3u8d5svE7eeaEyfMY6+JPHYbCZTkdS6I9X4hLbiytFBJDnqLBEF6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c15aaaea693e399f65b2bd62782599a46d9906c17091a2d539bbf439492fd923","last_reissued_at":"2026-07-05T11:50:36.854254Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:36.854254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.15103","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":"2026-07-05T11:50:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yfGornrwCWijVUNF8n0vJF9ptt3raDjXxSxxFGRn1xHf/xpD+zdrPVduO1QiKI/daSVJuMnov7ArrMQBVk8UBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T23:49:20.761133Z"},"content_sha256":"b03e13a096613456a058d0517a6b249f9be0a39ab2131fc0b8e3a8eb424f6f60","schema_version":"1.0","event_id":"sha256:b03e13a096613456a058d0517a6b249f9be0a39ab2131fc0b8e3a8eb424f6f60"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YFNKV2TJHY4Z6ZNSXVRHQJMZUR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Boxun Xu, Hejia Geng, Peng Li, Zihu Wang","submitted_at":"2025-05-21T04:54:18Z","abstract_excerpt":"Graph contrastive learning (GCL) has demonstrated great promise for learning generalizable graph representations from unlabeled data. However, conventional GCL approaches face two critical limitations: (1) the restricted expressive capacity of multilayer perceptron (MLP) based encoders, and (2) suboptimal negative samples that either from random augmentations-failing to provide effective 'hard negatives'-or generated hard negatives without addressing the semantic distinctions crucial for discriminating graph data. To this end, we propose Khan-GCL, a novel framework that integrates the Kolmogor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15103","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/2505.15103/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":"2026-07-05T11:50:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jOwyrCldjjzzCNh5DJIZpt/3mskh2fduXn8fd+48dTPgOIRPGJuMTRwQgH6kIGdycBcufunSiQZZteKZDasuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T23:49:20.761643Z"},"content_sha256":"38672884054bce2677bd339c533456f72641fc2c01b5cef90ab2d7546110cb84","schema_version":"1.0","event_id":"sha256:38672884054bce2677bd339c533456f72641fc2c01b5cef90ab2d7546110cb84"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR/bundle.json","state_url":"https://pith.science/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR/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":"2026-08-09T23:49:20Z","links":{"resolver":"https://pith.science/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR","bundle":"https://pith.science/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR/bundle.json","state":"https://pith.science/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YFNKV2TJHY4Z6ZNSXVRHQJMZUR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YFNKV2TJHY4Z6ZNSXVRHQJMZUR","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":"0108c6d11110a3e531c5b1fd75f9777f14ade69d91f6766903dae1ddf2a722f8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T04:54:18Z","title_canon_sha256":"ade8dccb89b2548c787dbfc2563aa8b7427e273970586fe23a40b906e4e0dc12"},"schema_version":"1.0","source":{"id":"2505.15103","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15103","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15103v2","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15103","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"pith_short_12","alias_value":"YFNKV2TJHY4Z","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"pith_short_16","alias_value":"YFNKV2TJHY4Z6ZNS","created_at":"2026-07-05T11:50:36Z"},{"alias_kind":"pith_short_8","alias_value":"YFNKV2TJ","created_at":"2026-07-05T11:50:36Z"}],"graph_snapshots":[{"event_id":"sha256:38672884054bce2677bd339c533456f72641fc2c01b5cef90ab2d7546110cb84","target":"graph","created_at":"2026-07-05T11:50:36Z","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/2505.15103/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph contrastive learning (GCL) has demonstrated great promise for learning generalizable graph representations from unlabeled data. However, conventional GCL approaches face two critical limitations: (1) the restricted expressive capacity of multilayer perceptron (MLP) based encoders, and (2) suboptimal negative samples that either from random augmentations-failing to provide effective 'hard negatives'-or generated hard negatives without addressing the semantic distinctions crucial for discriminating graph data. To this end, we propose Khan-GCL, a novel framework that integrates the Kolmogor","authors_text":"Boxun Xu, Hejia Geng, Peng Li, Zihu Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T04:54:18Z","title":"Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15103","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:b03e13a096613456a058d0517a6b249f9be0a39ab2131fc0b8e3a8eb424f6f60","target":"record","created_at":"2026-07-05T11:50:36Z","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":"0108c6d11110a3e531c5b1fd75f9777f14ade69d91f6766903dae1ddf2a722f8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T04:54:18Z","title_canon_sha256":"ade8dccb89b2548c787dbfc2563aa8b7427e273970586fe23a40b906e4e0dc12"},"schema_version":"1.0","source":{"id":"2505.15103","kind":"arxiv","version":2}},"canonical_sha256":"c15aaaea693e399f65b2bd62782599a46d9906c17091a2d539bbf439492fd923","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c15aaaea693e399f65b2bd62782599a46d9906c17091a2d539bbf439492fd923","first_computed_at":"2026-07-05T11:50:36.854254Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:50:36.854254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pJf332frCxhG5obnx4Ai18O9n3iBWUox3u8d5svE7eeaEyfMY6+JPHYbCZTkdS6I9X4hLbiytFBJDnqLBEF6AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:50:36.854749Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.15103","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b03e13a096613456a058d0517a6b249f9be0a39ab2131fc0b8e3a8eb424f6f60","sha256:38672884054bce2677bd339c533456f72641fc2c01b5cef90ab2d7546110cb84"],"state_sha256":"544acfca1550d3c317375072ed16bb19a19d617694e05e172a8be7a897cf295c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p9rbvGsXFuFOn55dde3HMOZedcZ0vQsPZ/sIegxXg0T9kqKHfjhf8n0cuUQkSPF75WALO4OsJQtZ3kUtUIhLAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T23:49:20.766778Z","bundle_sha256":"3d9fe8899d3b6f0d0555e763d2e9a74cbf5cffea9b7a13c9c20fa25e12ed25fb"}}