Social Network Clustering
Автор: Narges Azizifard
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 1 Vol. 6, 2014 года.
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As we know, the datasets related to social networks are increasing. There are different procedures to analyze these types of datasets; one of these procedures is clustering which makes communities of social data. Random walk is a process which can find communities in a network, in other words when a random walk is used, it scans the nodes in some steps; it begins with an initial node and based on a random process progresses to neighboring nodes. In this paper an algorithm is proposed which aims to finding communities in a way that modularity factor increases, for this goal, random walks with random local search agent are combined. Experimental results show that the proposed method gives better modularity in comparison with other algorithms.
Social Networks, Clustering, Community, Modularity, Random Walks
Короткий адрес: https://sciup.org/15012020
IDR: 15012020
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