Hierarchical Agglomerative Community Detection Algorithm Based on Similarity Modularity

Authors

Wenwei Zhan, College of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
Jingke Xi, College of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
Zhixiao Wang, College of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;

Abstract

Abstract: Fast Unfolding is a hierarchical community detection algorithm based on modularity. It runs very fast, but the accuracy needs to be improved. Because the algorithm adopts traditional modularity to merger communities, it only considers node link information and ignores the neighbor nodes. Therefore, two nodes that have common neighbors and weak link information may not be merged, thus affecting the accuracy. In view of the shortcomings, a hierarchical agglomerative community detection algorithm based on similarity modularity was proposed through introducing optimized similarity to improve the modularity. It adopts NMI as the accuracy measurement. Experiments on the real network and LFR synthetic network show that the accuracy of detecting community is obviously improved.

Recommended Citation

Zhan, Wenwei; Xi, Jingke; and Wang, Zhixiao (2020) "Hierarchical Agglomerative Community Detection Algorithm Based on Similarity Modularity," Journal of System Simulation: Vol. 29: Iss. 5, Article 13.
DOI: 10.16182/j.issn1004731x.joss.201705013
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss5/13

First Page

1028

Revised Date

2016-08-08

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201705013

Last Page

1032

CLC

TP393

Recommended Citation

Zhan Wenwei, Xi Jingke, Wang Zhixiao. Hierarchical Agglomerative Community Detection Algorithm Based on Similarity Modularity[J]. Journal of System Simulation, 2017, 29(5): 1028-1032.

DOI

10.16182/j.issn1004731x.joss.201705013