  Graphs are ubiquitous in many applications, such as social networks,
knowledge graphs, smart grids, etc.. Graph neural networks (GNN) are the
current state-of-the-art for these applications, and yet remain obscure to
humans. Explaining the GNN predictions can add transparency. However, as many
graphs are not static but continuously evolving, explaining changes in
predictions between two graph snapshots is different but equally important.
Prior methods only explain static predictions or generate coarse or irrelevant
explanations for dynamic predictions. We define the problem of explaining
evolving GNN predictions and propose an axiomatic attribution method to
uniquely decompose the change in a prediction to paths on computation graphs.
The attribution to many paths involving high-degree nodes is still not
interpretable, while simply selecting the top important paths can be suboptimal
in approximating the change. We formulate a novel convex optimization problem
to optimally select the paths that explain the prediction evolution.
Theoretically, we prove that the existing method based on
Layer-Relevance-Propagation (LRP) is a special case of the proposed algorithm
when an empty graph is compared with. Empirically, on seven graph datasets,
with a novel metric designed for evaluating explanations of prediction change,
we demonstrate the superiority of the proposed approach over existing methods,
including LRP, DeepLIFT, and other path selection methods.
