{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:04:18Z","timestamp":1775815458511,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p>We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections between the expressivity of GNNs and the two-variable fragment of first-order logic with counting quantifiers (C2). We introduce a decision-tree based model which leverages an extension of C2 to distill interpretable logical classifiers from GNNs. We test our approach on multiple GNN architectures. The distilled models are interpretable, succinct, and attain similar accuracy to the underlying GNN. Furthermore, when the ground truth is expressible in C2, our approach outperforms the GNN.<\/jats:p>","DOI":"10.24963\/kr.2024\/86","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:30:28Z","timestamp":1729924228000},"page":"920-930","source":"Crossref","is-referenced-by-count":3,"title":["Logical Distillation of Graph Neural Networks"],"prefix":"10.24963","author":[{"given":"Alexander","family":"Pluska","sequence":"first","affiliation":[{"name":"TU Wien"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascal","family":"Welke","sequence":"additional","affiliation":[{"name":"TU Wien"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"G\u00e4rtner","sequence":"additional","affiliation":[{"name":"TU Wien"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sagar","family":"Malhotra","sequence":"additional","affiliation":[{"name":"TU Wien"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}","theme":"Artificial Intelligence","location":"Hanoi, Vietnam","acronym":"KR-2024","number":"21","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"start":{"date-parts":[[2024,11,1]]},"end":{"date-parts":[[2024,11,8]]}},"container-title":["Proceedings of the TwentyFirst International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:30:47Z","timestamp":1729924247000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2024\/86"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2024\/86","relation":{},"subject":[],"published":{"date-parts":[[2024,11]]}}}