The exploitation of graph structures is the key to effectively learning
representations of nodes that preserve useful information in graphs. A
remarkable property of graph is that a latent hierarchical grouping of nodes
exists in a global perspective, where each node manifests its membership to a
specific group based on the context composed by its neighboring nodes. Most
prior works ignore such latent groups and nodes' membership to different
groups, not to mention the hierarchy, when modeling the neighborhood structure.
Thus, they fall short of delivering a comprehensive understanding of the nodes
under different contexts in a graph. In this paper, we propose a novel
hierarchical attentive membership model for graph embedding, where the latent
memberships for each node are dynamically discovered based on its neighboring
context. Both group-level and individual-level attentions are performed when
aggregating neighboring states to generate node embeddings. We introduce
structural constraints to explicitly regularize the inferred memberships of
each node, such that a well-defined hierarchical grouping structure is
captured. The proposed model outperformed a set of state-of-the-art graph
embedding solutions on node classification and link prediction tasks in a
variety of graphs including citation networks and social networks. Qualitative
evaluations visualize the learned node embeddings along with the inferred
memberships, which proved the concept of membership hierarchy and enables
explainable embedding learning in graphs.

本研究提出了一种基于邻域组织的层次化关注成员模型，用于图嵌入，通过引入结构约束，由上至下地捕获了一种良好定义的层次结构，实现了节点分类和链接预测任务的指标优于当前先进技术图嵌入解决方案，表明隐含群组和成员资格的概念在图嵌入学习中具有快速发展的潜力。