Macquarie University
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Dynamic community detection via evolutionary clustering

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posted on 2022-03-28, 12:49 authored by Fanzhen Liu
Dynamic community detection has been an efficient method to track the evolution of communities in dynamic social networks, which can be treated as an optimisation problem. Since evolutionary clustering (EC) was first proposed to optimise temporal data clustering, many EC-based algorithms have been developed to detect evolving community structure in dynamic social networks. However, there are two main drawbacks with existing EC based algorithms, which limit the efficiency and effectiveness of dynamic community detection: the classic operators cannot efficiently search for community structures and the general network presentation with an integer vector results in a limited search space. For this study, we first review recent literature regarding dynamic community detection using EC-based methods, and then develop two EC-based algorithms to efficiently detect evolving community structure by designing a migration operator in tandem with genetic operators and adopting a genome matrix-based representation for search space expansion. Compared with state-of-the-art baselines, our algorithms perform better in terms of clustering accuracy and temporal smoothness on both synthetic and real-world networks


Table of Contents

1 Introduction -- 2 Literature Review -- 3 ECD: Evolutionary Community Detection -- 4 DECS: Detecting Evolving Community Structure - An Improved ECD -- 5 Conclusion


Theoretical thesis. Bibliography: pages 49-55

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Jia Wu

Additional Supervisor 1

Jian Yang


Copyright 2019




1 online resource (xx, 55 pages) illustrations

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