Community Detection via Improved Genetic Algorithm in Complex Network

2012 ◽  
Vol 11 (3) ◽  
pp. 384-387 ◽  
Author(s):  
Shangguang Wang ◽  
Hua Zou ◽  
Qibo Sun ◽  
Xilu Zhu ◽  
Fangchun Yang
Keyword(s):  
Genetic Algorithm ◽  
Complex Network ◽  
Community Detection ◽  
Improved Genetic Algorithm

scholarly journals Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1472 ◽  
Author(s):  
Manuel Guerrero ◽  
Raul Baños ◽  
Consolación Gil ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Power Systems ◽  
Community Detection ◽  
North American ◽  
Flow Analysis ◽  
Power Grids ◽  
Improved Genetic Algorithm ◽  
Continental Scale ◽  
The North

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).

Community detection based on modularity and an improved genetic algorithm

2013 ◽  
Vol 392 (5) ◽  
pp. 1215-1231 ◽  
Author(s):  
Ronghua Shang ◽  
Jing Bai ◽  
Licheng Jiao ◽  
Chao Jin
Keyword(s):  
Genetic Algorithm ◽  
Community Detection ◽  
Improved Genetic Algorithm

An Effective Community Detection Method Based on Improved Genetic Algorithm

2014 ◽  
Vol 568-570 ◽  
pp. 852-857
Author(s):  
Lu Wang ◽  
Yong Quan Liang ◽  
Qi Jia Tian ◽  
Jie Yang ◽  
Chao Song ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Community Structure ◽  
Community Detection ◽  
Detection Method ◽  
Initial Population ◽  
Crossover Operator ◽  
Improved Genetic Algorithm ◽  
Mutation Process ◽  
Effective Community ◽  
New Community

Detecting community structure from complex networks has triggered considerable attention in several application domains. This paper proposes a new community detection method based on improved genetic algorithm (named CDIGA), which tries to find the best community structure by maximizing the network modularity. String encoding is used to realize genetic representation. Parts of nodes assign their community identifiers to all of their neighbors to ensure the convergence of the algorithm and eliminate unnecessary iterations when initial population is created. Crossover operator and mutation operator are improved too, one-way crossover strategy is introduced to crossover process, the Connect validity of mutation node is ensured in mutation process. We compared it with three other algorithms in computer generated networks and real world networks, Experiment Results show that the improved algorithm is highly effective for discovering community structure.

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