MA-Net: A reliable memetic algorithm for community detection by modularity optimization
- Publisher:
- Springer International Publishing
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1, 2014, pp. 311 - 323
- Issue Date:
- 2014
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| IES-2014-Leila.Moslemi-revised.pdf | Published version | 554.42 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
The information that can be transformed in knowledge from data in challenging real-world problems follows the accelerated rate of the advancement of technology in many different fields from biology to sociology. Complex networks are a useful representation of many problems in these domains One of the most important and challenging problems in network analysis lies in detecting community structures. This area of algorithmic research has attracted great attention due to its possible application in many fields. In this study we propose the MA-Net, memetic algorithm to detect communities in network by optimizing modularity value which is fast and reliable in the sense that it consistently produces sound solutions. Experiments using well-known real-world benchmark networks indicate that in comparison with other state-of-the-art algorithms, MA-Net has an outstanding performance on detecting communities.
Please use this identifier to cite or link to this item: