Open AccessGreedy Modularity Graph Clustering for Community Detection of Large Co-Authorship Network
Author(s) -
Ahmed F. Al-Mukhtar,
Eman S. Al-Shamery
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.19.28058
Subject(s) - modularity (biology) , community structure , computer science , cluster analysis , graph , clique percolation method , similarity (geometry) , complex network , clustering coefficient , data mining , set (abstract data type) , theoretical computer science , greedy algorithm , artificial intelligence , mathematics , algorithm , image (mathematics) , combinatorics , genetics , world wide web , biology , programming language
Social networks as a domain of complex networks that can be represented as graphs according to the patterns of connections among their elements. Social Communities are a set of nodes with denser connections inside community structures than outside. The goal of graph clustering is to divide the large graph into many clusters depending on multiple similarity criteria. In this work an improved version of the Louvain method is proposed, the Greedy Modularity Graph Clustering for Community Detection of Large Co-AuthorshipNetwork (GMGC)which introduces a new concept of weighted edges to enhance the accuracy of the Community Discovery for the large networks. The method is compared with other states of art methods mainly, Vertices Similarity First and Community Mean (VSFCM), and Generalized Louvain method for community detection in large networks (FKCD). Extensive experimental results have been madeon different datasets. The experimental results showed that the proposed method outperforms the other states of arts comparative methods according to the modularity optimization and community partitions evaluations measures.