  Graph Convolutional Networks (GCNs) achieved tremendous success by
effectively gathering local features for nodes. However, commonly do GCNs focus
more on node features but less on graph structures within the neighborhood,
especially higher-order structural patterns. However, such local structural
patterns are shown to be indicative of node properties in numerous fields. In
addition, it is not just single patterns, but the distribution over all these
patterns matter, because networks are complex and the neighborhood of each node
consists of a mixture of various nodes and structural patterns.
Correspondingly, in this paper, we propose Graph Structural-topic Neural
Network, abbreviated GraphSTONE, a GCN model that utilizes topic models of
graphs, such that the structural topics capture indicative graph structures
broadly from a probabilistic aspect rather than merely a few structures.
Specifically, we build topic models upon graphs using anonymous walks and Graph
Anchor LDA, an LDA variant that selects significant structural patterns first,
so as to alleviate the complexity and generate structural topics efficiently.
In addition, we design multi-view GCNs to unify node features and structural
topic features and utilize structural topics to guide the aggregation. We
evaluate our model through both quantitative and qualitative experiments, where
our model exhibits promising performance, high efficiency, and clear
interpretability.
