Boundary-connection deletion strategy based method for community detection in complex networks
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Boundary-connection deletion strategy based method for community detection in complex networks Chao Yuan1,2 · Chuitian Rong1,2 · Qingshuang Yao1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Community detection in complex networks is a difficult problem. Up to now, there is no very effective method to solve it. Recently, many community detection algorithms based on edge removal have been proposed. However, these edge removal methods often delete many key connections within communities and weaken (or destroy) the community structure of the network. This will make the network communities more difficult to be identified and reduce the accuracy and stability of the algorithm. This paper proposed a boundary connection deletion based community detection algorithm. Different from other algorithms, our algorithm focuses on identifying and removing the boundary connections between network modules. This can enhance the network community structure and get high quality network modules. With high performance, our algorithm can detect the optimal and hierarchical community structure in weighted networks simultaneously. In order to verify the effectiveness of our algorithm, the stability and robustness of our algorithm were firstly analyzed. Then a series of experiments had been done on the real-world and synthetic networks. The real-world networks include Zachary’s karate club network, dolphin social network, American college foot-ball network, PolBooks network, Les Mis´erables character network, and the coauthorship network of scientists; The synthetic networks include GN benchmark and LFR benckmark. Two indices NMI and Modularity Q were used to compare our algorithm with the recently proposed algorithms, including meta-LPAm+, Srinivas and Rajendran’s model, IDPM, CFCDs, EDCD, CNM, and CDASS. Experimental results show that our algorithm has better performance than these algorithms. Keywords Complex network · Community detection · Hierarchical community structure · Clustering · Boundary connection deletion strategy
1 Introduction Community detection in complex networks is a difficult but significant issue. It is helpful to analyze the structural and dynamic characteristics of complex networks. Besides, it also belongs to the clustering problem in machine learning and has potential applications in many engineering fields, such as sociological analysis, traffic network optimization and text document clustering. Take text document clustering for example, many studies [1–4] show that this problem Chao Yuan
[email protected] 1
School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China
2
Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, 300387, China
can be transformed into community detection problem on complex networks by representing documents as nodes and relationships as edges. This method has been proved to be effective in solving this kind of problems. The subject of community detection was first raised by
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