A New Approach to Community Detection in Complex Networks by Using Memetic Algorithms

A New Approach to Community Detection in Complex Networks by Using Memetic Algorithms

Ali Ghorbanian Mehdi Neyestani 

Department of Industrial Engineering, Esfarayen University of technology, Iran, North Khorasan 96619-98195

Department of Electrical Engineering, Esfarayen University of technology, North Khorasan 96619-98195

Corresponding Author Email: 
ali.ghorbanian83@gmail.com, m.Neyestani@esfarayen.ac.ir
26 December 2017
4 January 2018
30 September 2017
| Citation



Nowadays, networks are widely used to show the structure and type of relationship in various fields of science including social sciences, engineering, and social network. Investigating the structure of these networks has always been of interest to many people. To this end, the present study tries to identify these networks as interconnected communities, while each community might have unique properties. In this regard various methods and algorithms with different evaluation criteria are used, with the evolutionary algorithm having the highest share. The present research attempts to introduce a relatively efficient method for community detection through introduction of a new evaluation criterion based on the modularity density and giving weight to the relationships between nodes in the unweighted networks. For this purpose, at first we propose a new modularity based on difference of weight of nodes in a same community with other community also for this we propose a method for calculation weight between nodes in a same community named α_ij and between nodes in different community named β_ij. And employing this new modularity as the evaluation criteria by using Memetic Algorithm(MA). The proposed algorithms are evaluated in very complex artificial and real networks and the results were analyzed and were compared to other algorithms. For accuracy of the algorithm the number of communities is identified also Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) criteria are used to evaluate the algorithm. The obtained results indicated that MA based on new density is effective and efficient at detecting the community structure in complex real and artificial networks.


Complex networks, Modularity density, Memetic algorithm, Unweighted networks

1. Introductions
2. Modularity Density
3. Genetic and Memetic Algorithm
4. Adjusting Parameter’s Algorithms
5. Examining the Results
6. Results
7. Conclusion

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