  Prior works on formalizing explanations of a graph neural network (GNN) focus
on a single use case - to preserve the prediction results through identifying
important edges and nodes. In this paper, we develop a multi-purpose
interpretation framework by acquiring a mask that indicates topology
perturbations of the input graphs. We pack the framework into an interactive
visualization system (GNNViz) which can fulfill multiple purposes:
Preserve,Promote, or Attack GNN's predictions. We illustrate our approach's
novelty and effectiveness with three case studies: First, GNNViz can assist non
expert users to easily explore the relationship between graph topology and
GNN's decision (Preserve), or to manipulate the prediction (Promote or Attack)
for an image classification task on MS-COCO; Second, on the Pokec social
network dataset, our framework can uncover unfairness and demographic biases;
Lastly, it compares with state-of-the-art GNN explainer baseline on a synthetic
dataset.
