An efficient multiobjective evolutionary algorithm for community detection in social networks

2011 ◽  
Author(s):  
Babak Amiri ◽  
Liaquat Hossain ◽  
John W. Crawford
Keyword(s):  
Social Networks ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Multiobjective Evolutionary Algorithm

scholarly journals Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Jingjing Ma ◽  
Jie Liu ◽  
Wenping Ma ◽  
Maoguo Gong ◽  
Licheng Jiao
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Dynamic Networks ◽  
Time Step ◽  
Current Time ◽  
Multiobjective Evolutionary Algorithm ◽  
Dynamic Social Networks

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.

scholarly journals A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Guoqiang Chen ◽  
Yuping Wang ◽  
Jingxuan Wei
Keyword(s):  
Evolutionary Algorithm ◽  
Community Detection ◽  
Dynamic Networks ◽  
Experimental Studies ◽  
Detection Methods ◽  
Genetic Operators ◽  
Normalized Mutual Information ◽  
Multiobjective Evolutionary Algorithm ◽  
Important Research Topic ◽  
Synthetic Datasets

Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.

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