Improving the interpretability of GNN predictions through
conformal-based graph sparsification
Improving the interpretability of GNN predictions through
conformal-based graph sparsification
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks. However, most GNN architectures aggregate information from all nodes and edges in a graph, regardless of their relevance to the task at hand, thus hindering the interpretability of their predictions. In contrast to prior work, in this …