{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:T5GD4E6KTGQKTGTZZDBSQ3VQW7","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":"9e2ca2b0a9e549375912763f0a005a1b04538641b1acc035184c38a155c5f96f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-14T14:48:53Z","title_canon_sha256":"12e9e1bc115a215ac39a38579cc5df594022b02dad94dfd05ecec5a9aa7617c5"},"schema_version":"1.0","source":{"id":"1908.05153","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.05153","created_at":"2026-07-05T00:20:53Z"},{"alias_kind":"arxiv_version","alias_value":"1908.05153v2","created_at":"2026-07-05T00:20:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05153","created_at":"2026-07-05T00:20:53Z"},{"alias_kind":"pith_short_12","alias_value":"T5GD4E6KTGQK","created_at":"2026-07-05T00:20:53Z"},{"alias_kind":"pith_short_16","alias_value":"T5GD4E6KTGQKTGTZ","created_at":"2026-07-05T00:20:53Z"},{"alias_kind":"pith_short_8","alias_value":"T5GD4E6K","created_at":"2026-07-05T00:20:53Z"}],"graph_snapshots":[{"event_id":"sha256:95486064da3e6ebda7c085a98db7bf3d1df62c9ca1ce7f106907ff81d8f12e3d","target":"graph","created_at":"2026-07-05T00:20:53Z","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/1908.05153/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Convolutional Networks (GCNs) have been widely studied for compact data representation and semi-supervised learning tasks. However, existing GCNs usually use a fixed neighborhood graph which is not guaranteed to be optimal for semi-supervised learning tasks. In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation. Based on this observation, we then propose a unified adaptive neighborhood feature propagation model and derive a novel Adaptive Neighborhood Graph Propagation Network (ANGPN) for data repr","authors_text":"Bin Luo, Bo Jiang, Jin Tang, Leiling Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-14T14:48:53Z","title":"Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05153","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:c2f77ccbea8e60363eedae221c346e846469cbc3f3546eb56fb07e40d5b5ace4","target":"record","created_at":"2026-07-05T00:20:53Z","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":"9e2ca2b0a9e549375912763f0a005a1b04538641b1acc035184c38a155c5f96f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-14T14:48:53Z","title_canon_sha256":"12e9e1bc115a215ac39a38579cc5df594022b02dad94dfd05ecec5a9aa7617c5"},"schema_version":"1.0","source":{"id":"1908.05153","kind":"arxiv","version":2}},"canonical_sha256":"9f4c3e13ca99a0a99a79c8c3286eb0b7fbd8633a65daf12d3523bf2535b32573","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f4c3e13ca99a0a99a79c8c3286eb0b7fbd8633a65daf12d3523bf2535b32573","first_computed_at":"2026-07-05T00:20:53.958823Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:20:53.958823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3dzXaGQVWPlUJ7LpUKYCOZMJQp8erlO2+/yEso6hGcqYzDYeJem/Ynnp2eTT7ypBg+qFHm1loFa2MFUuh2nCBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:20:53.959284Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.05153","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c2f77ccbea8e60363eedae221c346e846469cbc3f3546eb56fb07e40d5b5ace4","sha256:95486064da3e6ebda7c085a98db7bf3d1df62c9ca1ce7f106907ff81d8f12e3d"],"state_sha256":"cfc7e52d627999cd16a79fcb82c8c62a5e316fccb408f85197c9eab2fe2a3c20"}