Hybrid Hierarchical Clustering Approach for Community Detection in Social Network
Abstract:Social Networks generally present a hierarchy of
communities. To determine these communities and the relationship
between them, detection algorithms should be applied. Most of
the existing algorithms, proposed for hierarchical communities
identification, are based on either agglomerative clustering or
divisive clustering. In this paper, we present a hybrid hierarchical
clustering approach for community detection based on both
bottom-up and bottom-down clustering. Obviously, our approach
provides more relevant community structure than hierarchical
method which considers only divisive or agglomerative clustering
to identify communities. Moreover, we performed some comparative
experiments to enhance the quality of the clustering results and to
show the effectiveness of our algorithm.
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