{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OD2HGJQFLV73OK7P3I3ZD3UYJ3","short_pith_number":"pith:OD2HGJQF","schema_version":"1.0","canonical_sha256":"70f47326055d7fb72befda3791ee984ed0ac5583c9a0f3f1117722dc1120d89c","source":{"kind":"arxiv","id":"1906.09602","version":1},"attestation_state":"computed","paper":{"title":"Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ruo-Chun Tzeng, Shan-Hung Wu","submitted_at":"2019-06-23T16:14:22Z","abstract_excerpt":"We study the problem of detecting critical structures using a graph embedding model. Existing graph embedding models lack the ability to precisely detect critical structures that are specific to a task at the global scale. In this paper, we propose a novel graph embedding model, called the Ego-CNNs, that employs the ego-convolutions convolutions at each layer and stacks up layers using an ego-centric way to detects precise critical structures efficiently. An Ego-CNN can be jointly trained with a task model and help explain/discover knowledge for the task. We conduct extensive experiments and t"},"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":"1906.09602","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-23T16:14:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"190d1af7dd1751db5a2c77a5711ee40da334c2ccf566894b175937148177f221","abstract_canon_sha256":"7c42c63b85e38eb7dd9a71d59c16d468b0732b2980ff428a005262186a7b6979"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:37.412865Z","signature_b64":"qvc/evI3ksrmbVV8YRbPFkcQJLWNI3g3S06RSR0KUUumDgCNObviT2FTfr6QL4BWPYmq844X0/Xt4j34+62nBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70f47326055d7fb72befda3791ee984ed0ac5583c9a0f3f1117722dc1120d89c","last_reissued_at":"2026-05-17T23:42:37.412217Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:37.412217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ruo-Chun Tzeng, Shan-Hung Wu","submitted_at":"2019-06-23T16:14:22Z","abstract_excerpt":"We study the problem of detecting critical structures using a graph embedding model. Existing graph embedding models lack the ability to precisely detect critical structures that are specific to a task at the global scale. In this paper, we propose a novel graph embedding model, called the Ego-CNNs, that employs the ego-convolutions convolutions at each layer and stacks up layers using an ego-centric way to detects precise critical structures efficiently. An Ego-CNN can be jointly trained with a task model and help explain/discover knowledge for the task. We conduct extensive experiments and t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09602","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1906.09602","created_at":"2026-05-17T23:42:37.412343+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.09602v1","created_at":"2026-05-17T23:42:37.412343+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09602","created_at":"2026-05-17T23:42:37.412343+00:00"},{"alias_kind":"pith_short_12","alias_value":"OD2HGJQFLV73","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"OD2HGJQFLV73OK7P","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"OD2HGJQF","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3","json":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3.json","graph_json":"https://pith.science/api/pith-number/OD2HGJQFLV73OK7P3I3ZD3UYJ3/graph.json","events_json":"https://pith.science/api/pith-number/OD2HGJQFLV73OK7P3I3ZD3UYJ3/events.json","paper":"https://pith.science/paper/OD2HGJQF"},"agent_actions":{"view_html":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3","download_json":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3.json","view_paper":"https://pith.science/paper/OD2HGJQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.09602&json=true","fetch_graph":"https://pith.science/api/pith-number/OD2HGJQFLV73OK7P3I3ZD3UYJ3/graph.json","fetch_events":"https://pith.science/api/pith-number/OD2HGJQFLV73OK7P3I3ZD3UYJ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3/action/storage_attestation","attest_author":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3/action/author_attestation","sign_citation":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3/action/citation_signature","submit_replication":"https://pith.science/pith/OD2HGJQFLV73OK7P3I3ZD3UYJ3/action/replication_record"}},"created_at":"2026-05-17T23:42:37.412343+00:00","updated_at":"2026-05-17T23:42:37.412343+00:00"}