Disentangled Representation Learning for Structural Role Discovery

Roles are defined as the equivalent classes of isomorphic nodes in the network. They focus on the local connective patterns and describe the structural similarities between nodes, and learning role-based network embeddings can help to recognize identities or functions of entities in real-world networks. This field has been studied over the past decades, however, the existing role-based network embedding methods all concentrate too much on distinguishing node structures and ignore that nodes can belong to different roles even if they have the same local structures. Not only the classical network structure can influence the role of node, but also the emergence of different type of relationship between nodes has impact to it. We believe that roles are influenced by multiple independent factors, and nodes in the same roles should possess both similar local structures and entangled patterns with neighbors hidden in each factor, but most of the existing methods did not clearly point out this challenge. Therefore, we propose a disentangled framework based on graph neural networks to simultaneously model the local structures and interactive relationships in multiple factors to generate role-oriented network embeddings. We design a novel role-aware neighbor choosing mechanism to assign each neighbor a different interactive weight for each latent factor, which can measure its influence on roles. We conduct experiments on synthetic and real-world networks, and the results demonstrate the superiority and effectiveness of our model.