Abstract
Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
Highlights
Many real-world complex systems can be represented as complex networks
A static analysis is applied to the snapshots of the network at different time steps, and community evolution is introduced afterward to interpret the change of communities over time [6]
Each single run of label propagation method (LPM) or order statistics local optimization method (OSLOM) on a single time step nearly gets the same clustering with ground truth
Summary
Many real-world complex systems can be represented as complex networks. Networks could be modeled as graphs, where nodes (or vertices) represent the objects and edges (or links) represent the interactions among these objects. The area of complex networks has attracted many researchers from different fields such as physics, mathematics, biology, and sociology. A community is defined as a subset of the graph nodes which densely connect with each other and sparsely connect with the rest of the networks [2, 3]. A static analysis is applied to the snapshots of the network at different time steps, and community evolution is introduced afterward to interpret the change of communities over time [6]. Data from real-world networks are ambiguous and subject to noise. Under such scenarios, if an algorithm extracts community structure for each time step independently, it often results in community structures with a high temporal variation [7]
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
