Task-oriented attributed network embedding by multi-view features

Network embedding, also known as network representation learning, aims at defining low-dimensional, continuous vector representation of nodes to maximally preserve the network structure. Recent efforts attempt to extend network embedding to attributed networks where nodes are enriched with descriptors, to enhance interpretability. However, most of these efforts seldom consider the additional knowledge relevant to the aim of the downstream network analysis, i.e. task-related information. When they do, they are analysis-specific and thus lack adaptability to alternative tasks. In this article, a unified framework TANE is proposed to learn Task-oriented Attributed Network Embedding that jointly, maximally and consistently preserves multiple types of network information to generate rich nodes representations, robust to a variety of analyses. The framework can flexibly adapt to, and be readily modified for, different network-based tasks in an end-to-end way. The results of extensive experiments on well-known and commonly used datasets demonstrate that the proposed framework TANE can achieve superior performance over state-of-the-art methods in two commonly performed tasks: node classification and link prediction.