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http://hdl.handle.net/10397/79731
Title: | Learning latent factors for community identification and summarization | Authors: | He, TT Hu, L Chan, KCC Hu, PW |
Issue Date: | 2018 | Source: | IEEE access, 2018, v. 6, p. 30137-30148 | 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. | Keywords: | Network analysis Social network Complex network Graph clustering Community detection Community summarization Latent factor analysis |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | 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. Posted with permission of the publisher. 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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