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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Vydané v:Complexity (New York, N.Y.) Ročník 2019; číslo 2019; s. 1 - 16
Hlavní autori: Wang, Ning-Ning, Peng, Xiao-Long, Jin, Zhen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 2019
Hindawi
John Wiley & Sons, Inc
Wiley
Predmet:
Affinity
Algorithms
Clustering
Complexity
Computer simulation
Science
Statistical mechanics
Switching theory
ISSN:1076-2787, 1099-0526
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1076-2787
1099-0526
DOI:10.1155/2019/6946189