{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:URAH4LKAO3W4EWZKORIVJOWYPS","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":"e4ca7250b99125cbf77553cbd77aabce765c582de7287ff6f7ba591575b9aebd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-04-28T08:09:08Z","title_canon_sha256":"7c3873e8d4f5d81e26fe42f1511c1d5dfba9bba72b86fdfd301cb5b04902a732"},"schema_version":"1.0","source":{"id":"2304.14678","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.14678","created_at":"2026-07-05T06:05:20Z"},{"alias_kind":"arxiv_version","alias_value":"2304.14678v1","created_at":"2026-07-05T06:05:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.14678","created_at":"2026-07-05T06:05:20Z"},{"alias_kind":"pith_short_12","alias_value":"URAH4LKAO3W4","created_at":"2026-07-05T06:05:20Z"},{"alias_kind":"pith_short_16","alias_value":"URAH4LKAO3W4EWZK","created_at":"2026-07-05T06:05:20Z"},{"alias_kind":"pith_short_8","alias_value":"URAH4LKA","created_at":"2026-07-05T06:05:20Z"}],"graph_snapshots":[{"event_id":"sha256:571e2ec27ec9719ce68415f2d188967548a3da66028b9749f72d88c76f8076ed","target":"graph","created_at":"2026-07-05T06:05:20Z","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/2304.14678/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy for researchers and engineers to reproduce, redevelop, and compare inductive KGRL methods. The library, experimental methodologies, and model re-imp","authors_text":"Huajun Chen, Mingyang Chen, Wen Zhang, Zhen Yao, Zhiwei Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-04-28T08:09:08Z","title":"NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.14678","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":"sha256:32050647a583ff13bac95015abc37084b0ba7e7d66142cf1f2903248ac419c6a","target":"record","created_at":"2026-07-05T06:05:20Z","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":"e4ca7250b99125cbf77553cbd77aabce765c582de7287ff6f7ba591575b9aebd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-04-28T08:09:08Z","title_canon_sha256":"7c3873e8d4f5d81e26fe42f1511c1d5dfba9bba72b86fdfd301cb5b04902a732"},"schema_version":"1.0","source":{"id":"2304.14678","kind":"arxiv","version":1}},"canonical_sha256":"a4407e2d4076edc25b2a745154bad87c879bb85cb7d0deac8e702271a2c7d183","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4407e2d4076edc25b2a745154bad87c879bb85cb7d0deac8e702271a2c7d183","first_computed_at":"2026-07-05T06:05:20.026746Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:05:20.026746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AqUv05Zj+Yusa+2p8uh4Xmma8KIRajMQPCpD5Voc2M/7YwY4vc7CwQ56uFU7GHKWLoihuRwxf5O3tZEG9UmTBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:05:20.027164Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.14678","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32050647a583ff13bac95015abc37084b0ba7e7d66142cf1f2903248ac419c6a","sha256:571e2ec27ec9719ce68415f2d188967548a3da66028b9749f72d88c76f8076ed"],"state_sha256":"75b9fb1467612869236d9860fa8025ae887d1ef2172fc14a575b3cfc13c1a247"}