Identifying Communities in Dynamic Networks Using Information Dynamics

Entropy

Aman Ullah¹  Bin Wang¹  Jinfang Sheng¹  FaizaRiaz Khawaja¹  Zejun Sun¹ 

[1] School of Computer Science and Engineering, Central South University, Changsha 401302, China;

关键词: dynamic community detection;    information dynamics;    propagation;    cluster;   

DOI  :  10.3390/e22040425

来源: DOAJ

【 摘 要 】

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

【 授权许可】

Unknown