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Explaining GNN-based Recommendations in Logic
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AI
DOI：10.14778/3712221.3712237.png)
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Abstract
En 中文
This paper proposes Makex (MAKE senSE), a logic approach to explaining why a GNN-based model M(x, y) recommends item y to user x. It proposes a class of Rules for ExPlanations, denoted as REPs and defined with a graph pattern Q and dependency X-M (x, y), where X is a collection of predicates, and the model M(x, y) is treated as the consequence of the rule. Intuitively, given M(x, y), we discover pattern Q to identify relevant topology, and precondition X to disclose correlations, interactions and dependencies of vertex features; together they provide rationals behind prediction M (x, y), identifying what features are decisive for M 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 M as global explanations, and (c) provide a top-k 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.
Journal
P
IF:
3.3
Papers:
556
Citations:
1.2W