Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79731
Title: Learning latent factors for community identification and summarization
Authors: He, TT 
Hu, L
Chan, KCC 
Hu, PW 
Keywords: Network analysis
Social network
Complex network
Graph clustering
Community detection
Community summarization
Latent factor analysis
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2018, v. 6, p. 30137-30148 How to cite?
Journal: IEEE access 
Abstract: Network communities, which are also known as network clusters, are typical latent structures in network data. Vertices in each of these communities tend to interact more and share similar features with each other. Community identification and feature summarization are significant tasks of network analytics. To perform either of the two tasks, there have been several approaches proposed, taking into the consideration of different categories of information carried by the network, e.g., edge structure, node attributes, or both aforementioned. But few of them are able to discover communities and summarize their features simultaneously. To address this challenge, we propose a novel latent factor model for community identification and summarization (LFCIS). To perform the task, the LFCIS first formulates an objective function that evaluating the overall clustering quality taking into the consideration of both edge topology and node features in the network. In the objective function, the LFCIS also adopts an effective component that ensures those vertices sharing with both similar local structures and features to be located into the same clusters. To identify the optimal cluster membership for each vertex, a convergent algorithm for updating the variables in the objective function is derived and used by LFCIS. The LFCIS has been tested with six sets of network data, including synthetic and real networks, and compared with several state-of-the-art approaches. The experimental results show that the LFCIS outperforms most of the prevalent approaches to community discovery in social networks, and the LFCIS is able to identify the latent features that may characterize those discovered communities.
URI: http://hdl.handle.net/10397/79731
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2843726
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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The following publication He, T. T., Hu, L., Chan, K. C. C., & Hu, P. W.(2018). Learning latent factors for community identification and summarization. IEEE Access, 6, 30137-30148 is available at https://dx.doi.org/10.1109/ACCESS.2018.2843726
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