Multiplex heterogeneous network representation learning with unipath based global awareness neural network

Abstract

Network embedding has gained great popularity in tackling various network analytical tasks, such as link prediction and node classification. However, most existing works from heterogeneous networks ignore the relation heterogeneity with multiplex networks between multi-typed nodes. To tackle this challenge, this work proposes a Unipath based global Awareness neural Network (UAN) for attributed multiplex heterogeneous network embedding. Our UAN can automatically learn useful interactions of unipath networks and the base network. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments were conducted on real-world datasets in three different domains and various network analysis tasks were performed. Experimental results demonstrate the significant superiority of UAN against state-of-the-art embedding baselines in terms of all evaluation metrics.