Attributed Multi-layer Network Embedding

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Network embedding has gained much attention in recent years. Embedding network into a low-dimensional vector space has shown promising performance in many graph mining tasks such as node classification, link prediction, and community detection. However, in many real-world applications, a variety of networks could be abstracted and presented in a multilayer fashion with rich information, such as user profiles of friendship networks and textual content of citation networks. Most existing algorithms focus on single-layer networks or homogeneous networks with a single type of nodes and edges. They fail to leverage the rich attributes and consider the rich semantic correlations of the nodes which are among within-layer or cross-layer. In this paper, we exploit the rich semantic information embedded in the multi-layer network by means of meta-path-based proximities and leverage the rich source of attributes in the multi-layer network to improve network embedding. Specifically, the semantic correlations come from both within-layer and cross-layer node connections, and attribute proximity is considered to refine the homogeneity of nodes that belong to the same type. We propose a generic Attributed Multi-layer Network Embedding framework, which learns representations for nodes by capturing both the rich semantic correlations and attribute information simultaneously in a unified optimization framework. Our extensive experimental evaluations on real-world multi-layer networks demonstrate that the proposed framework achieves better performance compared with the state-of-the-art embedding algorithms.