Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

Uncovering rationales behind predictions of graph neural networks (GNNs) has received
increasing attention over the years. Existing literature mainly focus on selecting a subgraph …

The Shapley value is a prominent tool for interpreting black-box machine learning models
thanks to its strong theoretical foundation. However, for models with structured inputs, such …

Graph Neural Networks (GNNs) have received increasing attention due to their ability to
learn from graph-structured data. To open the black-box of these deep learning models, post …