A Differential Geometric View and Explainability of GNN on Evolving Graphs

arXiv:2403.06425v1 Announce Type: new
Abstract: 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 …

abstract art arxiv biochemistry cs.ai cs.lg differential evolution explainability gnn graph graph neural networks graphs networks neural networks prediction social social networks state state-of-the-art models type view vital