  Recently, Graph Neural Networks (GNNs) have significantly advanced the
performance of machine learning tasks on graphs. However, this technological
breakthrough makes people wonder: how does a GNN make such decisions, and can
we trust its prediction with high confidence? When it comes to some critical
fields, such as biomedicine, where making wrong decisions can have severe
consequences, it is crucial to interpret the inner working mechanisms of GNNs
before applying them. In this paper, we propose a model-agnostic model-level
explanation method for different GNNs that follow the message passing scheme,
GNNInterpreter, to explain the high-level decision-making process of the GNN
model. More specifically, GNNInterpreter learns a probabilistic generative
graph distribution that produces the most discriminative graph pattern the GNN
tries to detect when making a certain prediction by optimizing a novel
objective function specifically designed for the model-level explanation for
GNNs. Compared to existing works, GNNInterpreter is more flexible and
computationally efficient in generating explanation graphs with different types
of node and edge features, without introducing another blackbox or requiring
manually specified domain-specific rules. In addition, the experimental studies
conducted on four different datasets demonstrate that the explanation graphs
generated by GNNInterpreter match the desired graph pattern if the model is
ideal; otherwise, potential model pitfalls can be revealed by the explanation.
The official implementation can be found at
https://github.com/yolandalalala/GNNInterpreter.
