A comprehensive survey on community detection methods and applications in complex information networks

被引:0
|
作者
Diboune, Abdelhani [1 ]
Slimani, Hachem [2 ]
Nacer, Hassina [1 ]
Bey, Kadda Beghdad [3 ]
机构
[1] USTHB, MOVEP Lab, BP 32, Bab Ezzouar 16111, Algiers, Algeria
[2] Univ Bejaia, Fac Exact Sci, LIMED Lab, Bejaia 06000, Algeria
[3] Ecole Mil Polytech, Informat Syst Lab, BP 17, Bordj El Bahri 16046, Algiers, Algeria
关键词
Network community detection; Graph clustering; Machine learning techniques; Multi-objective optimization; Game theoretic approaches; Networks applications; PARTICLE SWARM OPTIMIZATION; MULTIOBJECTIVE EVOLUTIONARY ALGORITHM; LABEL PROPAGATION ALGORITHM; GAME-THEORETIC FRAMEWORK; SOCIAL NETWORKS; RECOMMENDATION METHOD; MATRIX FACTORIZATION; WEIGHTED SIMILARITY; MEMETIC ALGORITHM; GENETIC ALGORITHM;
D O I
10.1007/s13278-024-01246-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper extensively reviews the literature of community detection in complex networks and proposes a general classification describing the main models used for this purpose. Besides, a statistical study of the distribution of the recent relevant literature has been realized to picture the tendency of the models used by the main works published in the context of community detection. This mainly helped the understanding of the suitable community model to be used in each real-world network application. Furthermore, we establish a critical study of the state-of-the-art approaches according to the proposed classification. Moreover, we investigate the relevant applications of communities in networks and we establish a statistical study to illustrate the distribution of research works in the field of community detection. Finally, we discuss several open issues and future research directions of approaches and applications that would be worth investigating in the area of community detection.
引用
收藏
页数:47
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