Community detection in complex networks using evolutionary computation

Jia, Guanbo (2017). Community detection in complex networks using evolutionary computation. University of Birmingham. Ph.D.

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In real world many complex systems can be naturally represented as complex networks of which one distinctive feature is the community structure. The community detection, i.e., identifying the community structure, provides insight into the relationship and interaction between network function and topology and has become increasingly important in many scientific fields. In this thesis, we firstly propose a cooperative coevolutionary module identification algorithm named CoCoMi to address the scalability problem when detecting community structures in especially medium and large-scale complex networks. Secondly, we propose a consensus community detection algorithm based on the multimodal optimization and fast Surprise named CoCoMOS to detect community structures in complex networks. Thirdly, we propose an adaptive ensemble selection and multimodal optimization based consensus community detection algorithm named MASCOD to find high quality and stable consensus partitions of community structures in complex networks. The performance of these three proposed algorithms is evaluated on some well-known social, artificial and biological complex networks and experimental results demonstrate that all these three proposed algorithms have very competitive performance compared with other state-of-the-art community detection algorithms.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Computer Science
Funders: None/not applicable
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software


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