Simple-based Dynamic Decentralized Community Detection Algorithm in socially aware networks
Zenggang Xiong,
Mingyang Zeng,
Fang Xu,
Min Deng,
Xuemin Zhang,
Bin Zhou,
Yuanlin Lyu
Affiliations
Zenggang Xiong
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; Institute for AI Industrial Technology Research, Hubei Engineering University, Hubei Engineering University, Xiaogan,432000, China
Mingyang Zeng
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; School of Computer Science and Information Engineering, Hubei University, Wuhan Hubei 430062, China
Fang Xu
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; Institute for AI Industrial Technology Research, Hubei Engineering University, Hubei Engineering University, Xiaogan,432000, China; Corresponding author at: School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China.
Min Deng
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; Institute for AI Industrial Technology Research, Hubei Engineering University, Hubei Engineering University, Xiaogan,432000, China
Xuemin Zhang
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; Institute for AI Industrial Technology Research, Hubei Engineering University, Hubei Engineering University, Xiaogan,432000, China
Bin Zhou
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; Institute for AI Industrial Technology Research, Hubei Engineering University, Hubei Engineering University, Xiaogan,432000, China
Yuanlin Lyu
School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China; School of Computer Science and Information Engineering, Hubei University, Wuhan Hubei 430062, China
This paper introduces a novel, Simple-based Dynamic Decentralized Community Detection Algorithm (S-DCDA) for Socially Aware Networks. This algorithm aims to address the resource-intensive nature, instabilities and inaccuracies of traditional distributed community detection algorithms. The dynamics of decentralization is evident in the threefold nature of the algorithm: (i) each node of the community is the core of the entire network or community for a certain period of time dependent on their need, (ii) nodes are not centralized around themselves, requiring the consent of the other node to join a community, and (iii) Communities start from a single node to form an initial scale community, the number of nodes and the relationship among them are constantly changing. The algorithm requires low processor performance and memory capacity size of each node, to a certain extent, effectively improve the accuracy and stability of community detection and maintenance. Experimental results demonstrate that in comparison to classical and classical-based improved community detection algorithms, S-DCDA yields superior detection results.
