Shuiwang Ji

On Explainability of Graph Neural Networks via Subgraph Explorations

2021 · arXiv (Cornell University)

We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explaining the importance of graph nodes or edges but ignore …

Task-Agnostic Graph Explanations

2022 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph-structured data. …

Towards Deeper Graph Neural Networks

2020

Graph neural networks have shown significant success in the field of graph representation learning. Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Nevertheless, one layer of these neighborhood aggregation …