Graph Neural Networks (GNNs) draw their strength from explicitly modeling the
topological information of structured data. However, existing GNNs suffer from
limited capability in capturing the hierarchical graph representation which
plays an important role in graph classification. In this paper, we innovatively
propose hierarchical graph capsule network (HGCN) that can jointly learn node
embeddings and extract graph hierarchies. Specifically, disentangled graph
capsules are established by identifying heterogeneous factors underlying each
node, such that their instantiation parameters represent different properties
of the same entity. To learn the hierarchical representation, HGCN
characterizes the part-whole relationship between lower-level capsules (part)
and higher-level capsules (whole) by explicitly considering the structure
information among the parts. Experimental studies demonstrate the effectiveness
of HGCN and the contribution of each component.

本文提出了一种称为层次图胶囊网络（HGCN）的创新方法，它可以联合学习节点嵌入和提取图层次结构，通过识别每个节点下的异构因素建立分离的图胶囊，从而表征同一实体的不同属性，并通过显式考虑部件之间的结构信息来表征低层胶囊（部分）和高层胶囊（整体）之间的整体关系。实验结果表明 HGCN 的效果和每个组件的贡献。