Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks |
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| Institution: | 1. Beijing University of Posts and Telecommunications, Beijing, China;2. Singapore Management University, Singapore;3. Worcester Polytechnic Institute, USA;4. Alibaba Group, Hangzhou, China;1. School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, China;2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;3. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;4. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China;5. West China School of Medicine, Sichuan University, Chengdu 610041, China;6. School of Public Administration, Sichuan University, Chengdu 610065, China;2. Zayed University, United Arab Emirates |
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| Abstract: | Node clustering on heterogeneous information networks (HINs) plays an important role in many real-world applications. While previous research mainly clusters same-type nodes independently via exploiting structural similarity search, they ignore the correlations of different-type nodes. In this paper, we focus on the problem of co-clustering heterogeneous nodes where the goal is to mine the latent relevance of heterogeneous nodes and simultaneously partition them into the corresponding type-aware clusters. This problem is challenging in two aspects. First, the similarity or relevance of nodes is not only associated with multiple meta-path-based structures but also related to numerical and categorical attributes. Second, clusters and similarity/relevance searches usually promote each other.To address this problem, we first design a learnable overall relevance measure that integrates the structural and attributed relevance by employing meta-paths and attribute projection. We then propose a novel approach, called SCCAIN, to co-cluster heterogeneous nodes based on constrained orthogonal non-negative matrix tri-factorization. Furthermore, an end-to-end framework is developed to jointly optimize the relevance measures and co-clustering. Extensive experiments on real-world datasets not only demonstrate that SCCAIN consistently outperforms state-of-the-art methods but also validate the effectiveness of integrating attributed and structural information for co-clustering. |
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