  Graph Neural Networks (GNNs) have emerged as a prominent framework for graph
mining, leading to significant advances across various domains. Stemmed from
the node-wise representations of GNNs, existing explanation studies have
embraced the subgraph-specific viewpoint that attributes the decision results
to the salient features and local structures of nodes. However, graph-level
tasks necessitate long-range dependencies and global interactions for advanced
GNNs, deviating significantly from subgraph-specific explanations. To bridge
this gap, this paper proposes a novel intrinsically interpretable scheme for
graph classification, termed as Global Interactive Pattern (GIP) learning,
which introduces learnable global interactive patterns to explicitly interpret
decisions. GIP first tackles the complexity of interpretation by clustering
numerous nodes using a constrained graph clustering module. Then, it matches
the coarsened global interactive instance with a batch of self-interpretable
graph prototypes, thereby facilitating a transparent graph-level reasoning
process. Extensive experiments conducted on both synthetic and real-world
benchmarks demonstrate that the proposed GIP yields significantly superior
interpretability and competitive performance to~the state-of-the-art
counterparts. Our code will be made publicly available.
