A Framework for Detecting Weighted Communities in the International Trade Network

Lei Ming Yan; Jin Han

doi:10.4028/www.scientific.net/AMM.513-517.2059

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517A Framework for Detecting Weighted Communities in...

A Framework for Detecting Weighted Communities in the International Trade Network

Abstract:

Community discovery is a crucial task in social network analysis, especially in describing the evolution of social networks. Although some works have focused on finding the dynamic community, there are still some open problems need to be conquered, such as analyzing the dynamic and weighted community. In this paper, we propose a framework for analyzing weighted communities and their evolutions via clustering correlated weight vectors to enhance existing community detection algorithms. The International trade network is used to verify our framework. Experiments show that the framework discovers and captures the evolving behaviors with temporal elements and weight values.

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Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

2059-2062

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.2059

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Online since:

February 2014

Authors:

Lei Ming Yan*, Jin Han

Keywords:

Community, Data Mining (DM), Evolving Patterns, Social Network Mining

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* - Corresponding Author

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