Relation constrained attributed network embedding

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Network embedding aims at learning a low-dimensional dense vector for each node in the network. In recent years, it has attracted great research attention due to its wide applications. Most existing studies model the graph structure only and neglect the attribute information. Although several attributed network embedding methods take the node attribute into account, they mainly focus on the basic relations between the nodes and their attributes like a user and his/her interests (attributes). The composite relations between two nodes, and two nodes’ attributes, and the related nodes and their attributes, contain rich information and can enhance the performance of many network analysis tasks. For example, two scholars having the common interests as “nature language processing” and “knowledge graph” may collaborate in the future and there will be a new edge in the network. However, such important information is still under-exploited.To address this limitation, we propose a novel framework to exploit the abundant relation information to enhance attributed network embedding. The main idea is to employ the multiple types of relations in attributed networks as the constraints to improve the network representation. To this end, we first construct the composite relations between two nodes and their attributes in addition to the commonly used basic relations. We then develop a relation constrained attributed network (RCAN) framework to learn the node representations by constraining them with these relations. We conduct extensive experiments on three real-world datasets to show the effectiveness of our proposed RCAN as an attributed network embedding method for modeling various social networks. The results demonstrate that our method achieves significantly better performance than the state-of-the-art baselines in both the link prediction and node clustering tasks.