Identifying Communities in Dynamic Networks Using Information Dynamics

Abstract

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.

Incremental methods for community detection in both fully and growing dynamic networks

“…Modularity optimization [35,36,38,39,40,41,43,47,57] -Suffers from the resolution limit problem and degeneracy problem.…”

“…Table 11: Some weaknesses and strengths of incremental methods based on modularity optimization, density, and label propagation [52,30,40,63,4,46,18,10,70,16,55,6,68,47,54,42,36,8,57,65] 5.4 Advantages and disadvantages of incremental methods for community detection in both fully and growing dynamic networks…”

“…These methods used also snapshots when identifying groups, taking into account the communities found in the previous snapshot but avoiding the need to match them. Other methods designed for detecting community in dynamic networks work directly on temporal networks are the incremental approaches [10,42,57]. They start with an initial community, and then update for each incoming change the community structure.…”

“…Lastly, [57] proposed a new framework to detect communities in dynamic networks based on information dynamics. The framework uses information dynamic model to identify initial communities and to incrementally discover community structure of the network.…”

“…Incremental community detection methods in fully dynamic networks based on label/information propagation[30,63,4,57] Some abbreviations in Table6described in footnote: ISLPA19 , ESLPA 20 , DCDID 21 , CDID22 .…”