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Open AccessArticle

Identifying Communities in Dynamic Networks Using Information Dynamics

1
School of Computer Science and Engineering, Central South University, Changsha 401302, China
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School of Information Engineering, Pingdingshan University, Pingdingshan 462500, China
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Authors to whom correspondence should be addressed.
Entropy 2020, 22(4), 425; https://doi.org/10.3390/e22040425
Received: 29 February 2020 / Revised: 26 March 2020 / Accepted: 8 April 2020 / Published: 9 April 2020
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. View Full-Text
Keywords: dynamic community detection; information dynamics; propagation; cluster dynamic community detection; information dynamics; propagation; cluster
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Sun, Z.; Sheng, J.; Wang, B.; Ullah, A.; Khawaja, F. Identifying Communities in Dynamic Networks Using Information Dynamics. Entropy 2020, 22, 425.

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