Search Results for author:
Found 3 papers, 1 papers with code
Interpreting Graph Neural Networks with In-Distributed Proxies
A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
