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Edge Weight Method for Community Detection on Mixed Scale-Free Networks.
- Source :
-
International Journal on Artificial Intelligence Tools . Apr2015, Vol. 24 Issue 2, p-1. 24p. - Publication Year :
- 2015
-
Abstract
- In this paper, we proposed an edge weight method for performing a community detection on mixed scale-free networks.We use the phrase 'mixed scale-free networks' for networks where some communities have node degree that follows a power law similar to scale-free networks, while some have node degree that follows normal distribution. In this type of network, community detection algorithms that are designed for scale-free networks will have reduced accuracy because some communities do not have scale-free properties. On the other hand, algorithms that are not designed for scale-free networks will also have reduced accuracy because some communities have scale-free properties. To solve this problem, our algorithm consists of two community detection steps; one is aimed at extracting communities whose node degree follows power law distribution (scale-free), while the other one is aimed at extracting communities whose node degree follows normal distribution (non scale-free). To evaluate our method, we use NMI - Normalized Mutual Information - to measure our results on both synthetic and real-world datasets comparing with both scale-free and non scale-free community detection methods. The results show that our method outperforms all other based line methods on mixed scale-free networks. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02182130
- Volume :
- 24
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- International Journal on Artificial Intelligence Tools
- Publication Type :
- Academic Journal
- Accession number :
- 102340612
- Full Text :
- https://doi.org/10.1142/S0218213015400072