{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UJXM33ELB6H5IIPC6MG3JJ7MMJ","short_pith_number":"pith:UJXM33EL","schema_version":"1.0","canonical_sha256":"a26ecdec8b0f8fd421e2f30db4a7ec624bdb97db93a234112cb3c060dd54a852","source":{"kind":"arxiv","id":"2207.13586","version":3},"attestation_state":"computed","paper":{"title":"Encoding Concepts in Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LO"],"primary_cat":"cs.LG","authors_text":"Dmitry Kazhdan, Fabrizio Silvestri, Federico Siciliano, Gabriele Ciravegna, Lucie Charlotte Magister, Mateja Jamnik, Pietro Barbiero, Pietro Lio","submitted_at":"2022-07-27T15:34:14Z","abstract_excerpt":"The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Encoder Module, the first differentiable concept-discovery approach for graph networks. The proposed approach makes graph networks explainable by design by first discovering graph concepts and then using these to solve the task. Our results demonstrate that this approach allows graph networks to: (i) attain model a"},"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":"2207.13586","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-27T15:34:14Z","cross_cats_sorted":["cs.AI","cs.LO"],"title_canon_sha256":"e91ad063c7953523ab267a2dc1dc8fc2de4e55f204a0d06ffeff6eb9677eb9bd","abstract_canon_sha256":"f481c8ee3a25c29dfbaaeba4cf68a7391cf338dd2117576a7db3266cbddda865"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:46:39.572796Z","signature_b64":"k+YPg1Zlio2X3r60k1sapvU39SoHR89GnLs3J/JK+0yRHTJmRKUw6fndsO3Pd2CTvUe+fCM0r4T5NiCc5khdAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a26ecdec8b0f8fd421e2f30db4a7ec624bdb97db93a234112cb3c060dd54a852","last_reissued_at":"2026-07-05T04:46:39.572355Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:46:39.572355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Encoding Concepts in Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LO"],"primary_cat":"cs.LG","authors_text":"Dmitry Kazhdan, Fabrizio Silvestri, Federico Siciliano, Gabriele Ciravegna, Lucie Charlotte Magister, Mateja Jamnik, Pietro Barbiero, Pietro Lio","submitted_at":"2022-07-27T15:34:14Z","abstract_excerpt":"The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, however, they fail to make the model itself more interpretable. To fill this gap, we introduce the Concept Encoder Module, the first differentiable concept-discovery approach for graph networks. The proposed approach makes graph networks explainable by design by first discovering graph concepts and then using these to solve the task. Our results demonstrate that this approach allows graph networks to: (i) attain model a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.13586","kind":"arxiv","version":3},"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/2207.13586/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":"2207.13586","created_at":"2026-07-05T04:46:39.572413+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.13586v3","created_at":"2026-07-05T04:46:39.572413+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.13586","created_at":"2026-07-05T04:46:39.572413+00:00"},{"alias_kind":"pith_short_12","alias_value":"UJXM33ELB6H5","created_at":"2026-07-05T04:46:39.572413+00:00"},{"alias_kind":"pith_short_16","alias_value":"UJXM33ELB6H5IIPC","created_at":"2026-07-05T04:46:39.572413+00:00"},{"alias_kind":"pith_short_8","alias_value":"UJXM33EL","created_at":"2026-07-05T04:46:39.572413+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10669","citing_title":"In Defense of Information Leakage in Concept-based Models","ref_index":220,"is_internal_anchor":false},{"citing_arxiv_id":"2210.15304","citing_title":"Explaining the Explainers in Graph Neural Networks: a Comparative Study","ref_index":67,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ","json":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ.json","graph_json":"https://pith.science/api/pith-number/UJXM33ELB6H5IIPC6MG3JJ7MMJ/graph.json","events_json":"https://pith.science/api/pith-number/UJXM33ELB6H5IIPC6MG3JJ7MMJ/events.json","paper":"https://pith.science/paper/UJXM33EL"},"agent_actions":{"view_html":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ","download_json":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ.json","view_paper":"https://pith.science/paper/UJXM33EL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.13586&json=true","fetch_graph":"https://pith.science/api/pith-number/UJXM33ELB6H5IIPC6MG3JJ7MMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/UJXM33ELB6H5IIPC6MG3JJ7MMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ/action/storage_attestation","attest_author":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ/action/author_attestation","sign_citation":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ/action/citation_signature","submit_replication":"https://pith.science/pith/UJXM33ELB6H5IIPC6MG3JJ7MMJ/action/replication_record"}},"created_at":"2026-07-05T04:46:39.572413+00:00","updated_at":"2026-07-05T04:46:39.572413+00:00"}