Hierarchical Attention Based Semi-supervised Network Representation Learning

Abstract

Network Embedding is a process of learning low-dimensional representation vectors of nodes by comprehensively utilizing network characteristics. Besides structure properties, information networks also contain rich external information, such as texts and labels. However, most of the traditional learning methods do not consider this kind of information comprehensively, which leads to the lack of semantics of embeddings. In this paper, we propose a Semi-supervised Hierarchical Attention Network Embedding method, named as SHANE, which can incorporate external information in a semi-supervised manner. First, a hierarchical attention network is used to learn the text-based embeddings according to the content of nodes. Then, the text-based embeddings and the structure-based embeddings are integrated in a closed interaction way. After that, we further introduce the label information of nodes into the embedding learning, which can promote the nodes with the same label closed in the embedding space. Extensive experiments of link prediction and node classification are conducted on two real-world datasets, and the results demonstrate that our method outperforms other comparison methods in all cases.