A Differential Geometric View and Explainability of GNN on Evolving Graphs
This provides explainability for GNNs on evolving graphs, which is crucial for applications like social networks and biochemistry, though it is incremental as it builds on existing attribution and geometric techniques.
The paper tackles the problem of understanding how Graph Neural Networks (GNNs) respond to evolving graphs by proposing a differential geometric method to model distributional evolution as smooth curves on a manifold, with experiments showing better sparsity, faithfulness, and intuitiveness compared to state-of-the-art methods.
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital to formally model and understand how a trained GNN responds to graph evolution. We propose a smooth parameterization of the GNN predicted distributions using axiomatic attribution, where the distributions are on a low-dimensional manifold within a high-dimensional embedding space. We exploit the differential geometric viewpoint to model distributional evolution as smooth curves on the manifold. We reparameterize families of curves on the manifold and design a convex optimization problem to find a unique curve that concisely approximates the distributional evolution for human interpretation. Extensive experiments on node classification, link prediction, and graph classification tasks with evolving graphs demonstrate the better sparsity, faithfulness, and intuitiveness of the proposed method over the state-of-the-art methods.