March 13, 2024, 4:42 a.m. | Harish G. Naik, Jan Polster, Raj Shekhar, Tam\'as Horv\'ath, Gy\"orgy Tur\'an

cs.LG updates on

arXiv:2403.07849v1 Announce Type: new
Abstract: We formulate an XAI-based model improvement approach for Graph Neural Networks (GNNs) for node classification, called Explanation Enhanced Graph Learning (EEGL). The goal is to improve predictive performance of GNN using explanations. EEGL is an iterative self-improving algorithm, which starts with a learned "vanilla" GNN, and repeatedly uses frequent subgraph mining to find relevant patterns in explanation subgraphs. These patterns are then filtered further to obtain application-dependent features corresponding to the presence of certain subgraphs …

abstract algorithm arxiv classification cs.lg gnn gnns graph graph learning graph neural network graph neural networks improvement iterative mining network networks neural network neural networks node performance predictive type via xai

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