{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SK3NCJS3JRMQXBSAUMFRGPW7BQ","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":"fca74479bd903e22541fb5d3d04d43175ec830833eea265c49446590e5b55017","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-15T02:19:07Z","title_canon_sha256":"48f73fce61f293be246217a3576c9eab43bf6a87e4e41c90ecdc270a39733834"},"schema_version":"1.0","source":{"id":"2310.09705","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.09705","created_at":"2026-07-05T07:01:10Z"},{"alias_kind":"arxiv_version","alias_value":"2310.09705v1","created_at":"2026-07-05T07:01:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09705","created_at":"2026-07-05T07:01:10Z"},{"alias_kind":"pith_short_12","alias_value":"SK3NCJS3JRMQ","created_at":"2026-07-05T07:01:10Z"},{"alias_kind":"pith_short_16","alias_value":"SK3NCJS3JRMQXBSA","created_at":"2026-07-05T07:01:10Z"},{"alias_kind":"pith_short_8","alias_value":"SK3NCJS3","created_at":"2026-07-05T07:01:10Z"}],"graph_snapshots":[{"event_id":"sha256:f6aa6d8f205e01e936256c0a1cda167e3ebf41180bdd29a19f52502f87e17fc7","target":"graph","created_at":"2026-07-05T07:01:10Z","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/2310.09705/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hinder current SGNN-based signed graph representation learning: sparsity in signed graphs leaves latent structures undiscovered, unbalanced triangles pose representation difficulties for SGNN models, and real-world signed graph datasets often lack supplementary information like node labels and features. These constraints limit the potential of SGNN-based representation learning. We address these issues with data augmenta","authors_text":"Dong Hao, Jiamou Liu, Kaiqi Zhao, Shuyan Wan, Sijie Wang, Xianda Zheng, Xinrui Zhang, Zeyu Zhang","cross_cats":["cs.SI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-15T02:19:07Z","title":"SGA: A Graph Augmentation Method for Signed Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09705","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:d0e43969b64dde23f522f4aebd361d6c846d88fd3b50b3b03e5cb45a6ed4649d","target":"record","created_at":"2026-07-05T07:01:10Z","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":"fca74479bd903e22541fb5d3d04d43175ec830833eea265c49446590e5b55017","cross_cats_sorted":["cs.SI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-15T02:19:07Z","title_canon_sha256":"48f73fce61f293be246217a3576c9eab43bf6a87e4e41c90ecdc270a39733834"},"schema_version":"1.0","source":{"id":"2310.09705","kind":"arxiv","version":1}},"canonical_sha256":"92b6d1265b4c590b8640a30b133edf0c0bc714bbd4aaddb5e55e1554454de687","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"92b6d1265b4c590b8640a30b133edf0c0bc714bbd4aaddb5e55e1554454de687","first_computed_at":"2026-07-05T07:01:10.019940Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:10.019940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GYpu8CASvXBeS9bZFeYbNQt+gPIKGyvFMjaRNFjuo+L18fkgTi5tu8stYQ3ZTIZJGBFZted8Z8XAo8dvMeg5Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:10.020378Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.09705","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0e43969b64dde23f522f4aebd361d6c846d88fd3b50b3b03e5cb45a6ed4649d","sha256:f6aa6d8f205e01e936256c0a1cda167e3ebf41180bdd29a19f52502f87e17fc7"],"state_sha256":"d54d35efa8aacb2e397b2ea99a710dfa4dee6968a4f938adedfa0794f708688c"}