Score-based Explainability for Graph Representations

Ehsan Hajiramezanali, Sepideh Maleki, Max W Shen, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia

Abstract: Despite the widespread use of unsupervised Graph Neural Networks (GNNs), their post-hoc explainability remains underexplored. Current graph explanation methods typically focus on explaining a single dimension of the final output. However, unsupervised and self-supervised GNNs produce d-dimensional representation vectors whose individual elements lack clear, disentangled semantic meaning. To tackle this issue, we draw inspiration from the success of score-based graph explainers in supervised GNNs and propose a novel framework, grXAI, for graph representation explainability. grXAI generalizes existing score-based graph explainers to identify the subgraph most responsible for constructing the latent representation of the input graph. This framework can be easily and efficiently implemented as a wrapper around existing methods, enabling the explanation of graph representations through connected subgraphs, which are more human-intelligible. Extensive qualitative and quantitative experiments demonstrate grXAI's strong ability to identify subgraphs that effectively explain learned graph representations across various unsupervised tasks and learning algorithms.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Fuxin_Li1

Submission Number: 2844