{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7WU2NVUBVA53MXPMNRMB3ZXR3P","short_pith_number":"pith:7WU2NVUB","schema_version":"1.0","canonical_sha256":"fda9a6d681a83bb65dec6c581de6f1dbffa22791ba490faca25e0caceb6df36d","source":{"kind":"arxiv","id":"1904.11883","version":2},"attestation_state":"computed","paper":{"title":"Robust Graph Data Learning via Latent Graph Convolutional Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Bin Luo, Bo Jiang, Ziyan Zhang","submitted_at":"2019-04-26T15:05:45Z","abstract_excerpt":"Graph Convolutional Representation (GCR) has achieved impressive performance for graph data representation. However, existing GCR is generally defined on the input fixed graph which may restrict the representation capacity and also be vulnerable to the structural attacks and noises. To address this issue, we propose a novel Latent Graph Convolutional Representation (LatGCR) for robust graph data representation and learning. Our LatGCR is derived based on reformulating graph convolutional representation from the aspect of graph neighborhood reconstruction. Given an input graph $\\textbf{A}$, Lat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.11883","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-26T15:05:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b02435a681b41ba75b78bac1bdb787fda5f740970e42917a149e973ebc83b181","abstract_canon_sha256":"a08b894a9d4438922139345162a798c67be7c1aec2004f323367c4bba8a55b6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:22:18.597342Z","signature_b64":"rh9TONn54eWHTna0mt/eRTdo5NivbmiX9+Vf6z24JySLC3/rW3bk0RWQ0AOgWfQwCFCtR4qn8xdiDRPkqjmZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fda9a6d681a83bb65dec6c581de6f1dbffa22791ba490faca25e0caceb6df36d","last_reissued_at":"2026-07-05T03:22:18.596943Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:22:18.596943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Graph Data Learning via Latent Graph Convolutional Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Bin Luo, Bo Jiang, Ziyan Zhang","submitted_at":"2019-04-26T15:05:45Z","abstract_excerpt":"Graph Convolutional Representation (GCR) has achieved impressive performance for graph data representation. However, existing GCR is generally defined on the input fixed graph which may restrict the representation capacity and also be vulnerable to the structural attacks and noises. To address this issue, we propose a novel Latent Graph Convolutional Representation (LatGCR) for robust graph data representation and learning. Our LatGCR is derived based on reformulating graph convolutional representation from the aspect of graph neighborhood reconstruction. Given an input graph $\\textbf{A}$, Lat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11883","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/1904.11883/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.11883","created_at":"2026-07-05T03:22:18.596998+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.11883v2","created_at":"2026-07-05T03:22:18.596998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11883","created_at":"2026-07-05T03:22:18.596998+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WU2NVUBVA53","created_at":"2026-07-05T03:22:18.596998+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WU2NVUBVA53MXPM","created_at":"2026-07-05T03:22:18.596998+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WU2NVUB","created_at":"2026-07-05T03:22:18.596998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1910.01735","citing_title":"GmCN: Graph Mask Convolutional Network","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P","json":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P.json","graph_json":"https://pith.science/api/pith-number/7WU2NVUBVA53MXPMNRMB3ZXR3P/graph.json","events_json":"https://pith.science/api/pith-number/7WU2NVUBVA53MXPMNRMB3ZXR3P/events.json","paper":"https://pith.science/paper/7WU2NVUB"},"agent_actions":{"view_html":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P","download_json":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P.json","view_paper":"https://pith.science/paper/7WU2NVUB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.11883&json=true","fetch_graph":"https://pith.science/api/pith-number/7WU2NVUBVA53MXPMNRMB3ZXR3P/graph.json","fetch_events":"https://pith.science/api/pith-number/7WU2NVUBVA53MXPMNRMB3ZXR3P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P/action/storage_attestation","attest_author":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P/action/author_attestation","sign_citation":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P/action/citation_signature","submit_replication":"https://pith.science/pith/7WU2NVUBVA53MXPMNRMB3ZXR3P/action/replication_record"}},"created_at":"2026-07-05T03:22:18.596998+00:00","updated_at":"2026-07-05T03:22:18.596998+00:00"}