Graph neural networks (GNNs) have been extensively developed for graph representation
learning in various application domains. However, similar to all other neural networks …

Graph neural networks (GNNs) have demonstrated a significant boost in prediction
performance on graph data. At the same time, the predictions made by these models are …

Graphs neural networks (GNNs) learn node features by aggregating and combining
neighbor information, which have achieved promising performance on many graph tasks …

Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph
data. However, the black-box nature of GNNs prevents users from understanding and …

As one of the most popular machine learning models today, graph neural networks (GNNs)
have attracted intense interest recently, and so does their explainability. Users are …

Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging and nascent problem. The leading method mainly considers …

Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-
related tasks. However, the black-box nature often limits their interpretability and …

Abstract Graph Neural Networks (GNNs) have demonstrated their efficacy in representing
graph-structured data, but their lack of explainability hinders their applicability to critical …

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 …