Reinforced Causal Explainer for Graph Neural Networks
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DOI: 10.1109/tpami.2022.3170302
Abstract: Explainability is crucial for probing graph neural networks (GNNs), answering questions like “Why the GNN model makes a certain prediction?”. Feature attribution is a prevalent technique of highlighting the explanatory subgraph in the input graph,… read more here.
Keywords: neural networks; reinforced causal; causal explainer; subgraph ... See more keywords