Community Detection with Self-Adapting Switching Based on Affinity

Community structures in complex networks play an important role in researching network function. Although there are various algorithms based on affinity or similarity, their drawbacks are obvious. They perform well in strong communities, but perform poor in weak communities. Experiments show that so...

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Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2019; no. 2019; pp. 1 - 16
Main Authors Wang, Ning-Ning, Peng, Xiao-Long, Jin, Zhen
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2019
Hindawi
Hindawi Limited
Wiley
Subjects
Affinity
Algorithms
Clustering
Complexity
Computer simulation
Science
Statistical mechanics
Switching theory
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Summary:Community structures in complex networks play an important role in researching network function. Although there are various algorithms based on affinity or similarity, their drawbacks are obvious. They perform well in strong communities, but perform poor in weak communities. Experiments show that sometimes, community detection algorithms based on a single affinity do not work well, especially for weak communities. So we design a self-adapting switching (SAS) algorithm, where weak communities are detected by combination of two affinities. Compared with some state-of-the-art algorithms, the algorithm has a competitive accuracy and its time complexity is near linear. Our algorithm also provides a new framework of combination algorithm for community detection. Some extensive computational simulations on both artificial and real-world networks confirm the potential capability of our algorithm.
ISSN:1076-2787
1099-0526
DOI:10.1155/2019/6946189