Community Mining in Complex Network Based on Parallel Genetic Algorithm

Community mining has been the focus of many recent efforts on complex networks, and the genetic algorithm with low time-complexity is widely used in this discipline. To enhance the performance of genetic algorithm for community detection, the modified crossover operators which are more suitable for...

Full description

Saved in:
Bibliographic Details
Main Authors: XiLu Zhu, Bai Wang
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Communities
community mining
Complex networks
Convergence
evolutionary operator operator
genetic algorithm
Genetics
Heuristic algorithms
mapreduce
Optimization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Community mining has been the focus of many recent efforts on complex networks, and the genetic algorithm with low time-complexity is widely used in this discipline. To enhance the performance of genetic algorithm for community detection, the modified crossover operators which are more suitable for community detection is proposed in this paper, and the heuristic mutation operator based on local modularity is designed to avoid the blindness of random flip. Additionally, to avoid premature, an independent evolution model is implemented on Chain Map Reduce framework. The experimental results show that the distributed evolutionary model contributes to reduce the selection pressure and maintains the population's diversity. Moreover, the modified genetic operators improve the global optimization performance and quicken the convergence speed.
DOI:10.1109/ICGEC.2010.87