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Combined node and link partitions method for finding overlapping communities in complex networks.

Jin, D., Gabrys, B. and Dang, J., 2015. Combined node and link partitions method for finding overlapping communities in complex networks. Scientific Reports, 5.

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Scientific Reports.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.1038/srep08600


Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures.

Item Type:Article
Uncontrolled Keywords:computer science; complex networks
Group:Faculty of Science & Technology
ID Code:21851
Deposited By: Symplectic RT2
Deposited On:15 Apr 2015 13:42
Last Modified:14 Mar 2022 13:51


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