{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IQNKVXULNXJYKP2Y5LABELLJU3","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":"b12c6970c385892784a527b45db0932e6f3bd2b568412895babfd4c81d243911","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-02T01:20:54Z","title_canon_sha256":"4ecd93281d84e7d266b00d04322fe61a07482bdbb4ff7ea021df472d3513c351"},"schema_version":"1.0","source":{"id":"1908.00673","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.00673","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"arxiv_version","alias_value":"1908.00673v1","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.00673","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"pith_short_12","alias_value":"IQNKVXULNXJY","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"pith_short_16","alias_value":"IQNKVXULNXJYKP2Y","created_at":"2026-07-04T23:51:08Z"},{"alias_kind":"pith_short_8","alias_value":"IQNKVXUL","created_at":"2026-07-04T23:51:08Z"}],"graph_snapshots":[{"event_id":"sha256:f98a4828e4d62f4636629796f55dd84c5ab01f0cddbe4255a8147eaa16a0a8ed","target":"graph","created_at":"2026-07-04T23:51:08Z","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/1908.00673/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher order graph convolutional network has a large number of parameters and high computational complexity. Therefore, we propose a Hybrid Lower order and Higher order Graph convolutional networks (HLHG) learning model, which uses weight sharing mechanism to reduce the number of network parameters. To reduce computational complexity, we propose a novel fusion pooling layer to combine the neighborhood information of high o","authors_text":"Bingo Wing-Kuen Ling, FangYuan Lei, Huimin Zhao, QingYun Dai, Xun Liu, Yan Liu","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-02T01:20:54Z","title":"Hybrid Low-order and Higher-order Graph Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.00673","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:c71c63afa5a327f82b477f46adbd346562791aeb1a45aee03257b8978ca6a9a4","target":"record","created_at":"2026-07-04T23:51:08Z","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":"b12c6970c385892784a527b45db0932e6f3bd2b568412895babfd4c81d243911","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-02T01:20:54Z","title_canon_sha256":"4ecd93281d84e7d266b00d04322fe61a07482bdbb4ff7ea021df472d3513c351"},"schema_version":"1.0","source":{"id":"1908.00673","kind":"arxiv","version":1}},"canonical_sha256":"441aaade8b6dd3853f58eac0122d69a6e2da8c89de44f87c060005a5bdcfcf4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"441aaade8b6dd3853f58eac0122d69a6e2da8c89de44f87c060005a5bdcfcf4b","first_computed_at":"2026-07-04T23:51:08.175593Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:51:08.175593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4wErx+zqrk7ZXFpK1PSLFGoCfA2MDjG6bqNdgOn4iIWw1zNUghz+X7JBomV3FuqaFBi0PC5RC3qvZlIfkTkDCQ==","signature_status":"signed_v1","signed_at":"2026-07-04T23:51:08.176032Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.00673","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c71c63afa5a327f82b477f46adbd346562791aeb1a45aee03257b8978ca6a9a4","sha256:f98a4828e4d62f4636629796f55dd84c5ab01f0cddbe4255a8147eaa16a0a8ed"],"state_sha256":"8f166bee989a85b49338bf14da14bde610ef892964adcf46b1c8bfeb7c029d36"}