Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

Community structure is one of the most has received an enormous amount of attention in recent important properties in social networks, and community detection years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection...

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Bibliographic Details
Published in:Journal of computer science and technology 2012, Vol.27 (3), p.455-467
Main Author: 公茂果 张岭军 马晶晶 焦李成
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Clustering
Communities
Computer Science
Data Structures and Information Theory
Datasets
Dynamics
Evolution
Genetic algorithms
Genetics
Graph representations
Information Systems Applications (incl.Internet)
Modularity
Multiple objective analysis
Networks
Optimization
R&D
Regular Paper
Research & development
Social network analysis
Social networks
Software Engineering
Synthetic data
Tasks
Theory of Computation
免疫算法
动态网络
多目标
时间成本
检测
社会结构
社会网络
网络社区
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Summary:Community structure is one of the most has received an enormous amount of attention in recent important properties in social networks, and community detection years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms. In this paper, a novel nmltiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks. It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method. Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-012-1235-y