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EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
May 6, 2024, 4:42 a.m. | Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu
cs.LG updates on arXiv.org arxiv.org
Abstract: Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness. Subgraph-level explanations are gaining attention for their intuitive appeal. However, most existing subgraph-level explainers face efficiency challenges in explaining GNNs due to complex search processes. The key challenge is to find a balance between intuitiveness and efficiency while ensuring transparency. Additionally, these explainers usually induce subgraphs by nodes, which may introduce less-intuitive disconnected nodes in the subgraph-level …
abstract arxiv attention challenges cs.lg edge efficiency explainers face gnn gnns graph graph neural networks however key linear networks neural networks predictions processes safety search the key type understanding
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