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.
Highlights
Systems in the real world can be abstracted into complex networks, and a large number of algorithms for complex network mining have been proposed [1,2,3,4,5], most of which focus on static networks
The evaluation of the performance of the dynamic community detection is mainly to quantify the goodness of community detection in each time slice, and the evaluation criteria adopted are consistent with community detection in a static network
To cover several states of dynamic network community structure changes, we evaluate the comparison algorithms from four aspects: node switch, community birth and death, community expansion and contraction, and community merger and split
Summary
Systems in the real world can be abstracted into complex networks, and a large number of algorithms for complex network mining have been proposed [1,2,3,4,5], most of which focus on static networks. Studying the structural characteristics of dynamic networks is beneficial to analyze the formation mechanism of networks, explore the implicit structure, and predict the evolution of the network structure. These have important theoretical and practical value for the development of various fields and disciplines in real society. Researchers have conducted extensive research on community structures in complex networks and have proposed many well-known static community discovery methods, such as GN [6], CPM [7], LPA [8], Louvain [9], Infomap [10], MCL [11], etc. Most static community detection algorithms are not suitable for dynamic networks
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
