Abstract
Network community detection is one of the most fundamental problems in network structure analytics. With the modularity and modularity density being put forward, network community detection is formulated as a single-objective optimization problem and then communities of network can be discovered by optimizing modularity or modularity density. However, the community detection by optimizing modularity or modularity density is NP-hard. The computational intelligence algorithm, especially for evolutionary single-objective algorithms, have been effectively applied to discover communities from networks. This chapter focuses on evolutionary single-objective algorithms for solving network community discovery. First this chapter reviews evolutionary single-objective algorithm for network community discovery. Then three representative algorithms and their performances of discovering communities are introduced in detail.
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Notes
- 1.
Acknowledgement: Reprinted from Applied Soft Computing, 19, Ma, L., Gong, M., Liu, J., Cai, Q., Jiao, L., Multi-level learning based memetic algorithm for community detection, 121–133, Copyright (2014), with permission from Elsevier.
- 2.
Acknowledgement: Reprinted from Information Science, 316, Cai, Q., Gong, M., Ma, L., Ruan, S., Yuan, F., Jiao, L., Greedy discrete particle swarm optimization for large-scale social network clustering, 503-516, Copyright(2015), with permission from Elsevier.
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Gong, M., Cai, Q., Ma, L., Wang, S., Lei, Y. (2017). Network Community Discovery with Evolutionary Single-Objective Optimization. In: Computational Intelligence for Network Structure Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-4558-5_2
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DOI: https://doi.org/10.1007/978-981-10-4558-5_2
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