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VLDB Endow."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p>\n            This paper proposes Makex (MAKE senSE), a logic approach to explaining why a GNN-based model\n            <jats:italic>M<\/jats:italic>\n            (\n            <jats:italic>x, y<\/jats:italic>\n            ) recommends item\n            <jats:italic>y<\/jats:italic>\n            to user\n            <jats:italic>x.<\/jats:italic>\n            It proposes a class of Rules for ExPlanations, denoted as REPs and defined with a graph pattern\n            <jats:italic>Q<\/jats:italic>\n            and dependency X \u2192\n            <jats:italic>M<\/jats:italic>\n            (\n            <jats:italic>x, y<\/jats:italic>\n            ), where\n            <jats:italic>X<\/jats:italic>\n            is a collection of predicates, and the model\n            <jats:italic>M<\/jats:italic>\n            (\n            <jats:italic>x, y<\/jats:italic>\n            ) is treated as the consequence of the rule. Intuitively, given\n            <jats:italic>M<\/jats:italic>\n            (\n            <jats:italic>x, y<\/jats:italic>\n            ), we discover pattern\n            <jats:italic>Q<\/jats:italic>\n            to identify relevant topology, and precondition\n            <jats:italic>X<\/jats:italic>\n            to disclose correlations, interactions and dependencies of vertex features; together they provide rationals behind prediction\n            <jats:italic>M<\/jats:italic>\n            (\n            <jats:italic>x, y<\/jats:italic>\n            ), identifying what features are decisive for\n            <jats:italic>M<\/jats:italic>\n            to make predictions and under what conditions the decision can be made. We (a) define REPs with 1-WL test, on which most GNN models for recommendation are based; (b) develop an algorithm for discovering REPs for\n            <jats:italic>M<\/jats:italic>\n            as global explanations, and (c) provide a top-\n            <jats:italic>k<\/jats:italic>\n            algorithm to compute top-ranked local explanations. Using real-life graphs, we empirically verify that Makex outperforms previous explanation methods in terms of fidelity, sparsity and efficiency.\n          <\/jats:p>","DOI":"10.14778\/3712221.3712237","type":"journal-article","created":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T18:03:04Z","timestamp":1744048984000},"page":"715-728","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Explaining GNN-Based Recommendations in Logic"],"prefix":"10.14778","volume":"18","author":[{"given":"Wenfei","family":"Fan","sequence":"first","affiliation":[{"name":"Beihang University, China and Shenzhen Institute of Computing Sciences, China and University of Edinburgh, United Kingdom"}]},{"given":"Lihang","family":"Fan","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"given":"Dandan","family":"Lin","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, China"}]},{"given":"Min","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Computing Sciences, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"1995. 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