Community Detection Based on Markov Similarity Enhancement

Community detection can discover the cluster structure hidden in complex networks, which helps people predict network behavior and understand network functions. It is one of the current research hotspots. In this paper, we propose a Markov similarity enhancement method, which obtains the steady-stat...

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Bibliographic Details
Published inIEEE transactions on circuits and systems. II, Express briefs Vol. 70; no. 9; p. 1
Main Authors Yang, Xu-Hua, Ma, Gang-Feng, Zeng, Xiang-Yu, Pang, Yuchao, Zhou, Yanbo, Zhang, Yu-Di, Ye, Lei
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
community detection
Complex networks
Detection algorithms
Indexes
Iterative methods
local similarity
Markov processes
Markov similarity
Networks
Numerical simulation
Similarity
Social networking (online)
Steady-state
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Summary:Community detection can discover the cluster structure hidden in complex networks, which helps people predict network behavior and understand network functions. It is one of the current research hotspots. In this paper, we propose a Markov similarity enhancement method, which obtains the steady-state Markov similarity enhancement matrix through the Markov iterative state transition of the initial network. According to the Markov similarity index, the network is divided into initial community structure. Then, we merge the small communities into its closely connected communities to obtain the final community. On seven real networks and artificial networks with variable parameters, compared with other seven well-known community detection algorithms, numerical simulation experiments show that the proposed algorithm has a good community detection effect.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2023.3275153