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With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks,community detection in networks becomes a hot research topic in physics,sociology,computer society,etc. Although this investigation of community structures has motivated many diverse algorithms,most of them are unsuitable when dealing with large networks due to their computational cost.In this paper,we present a faster algorithm ComTector, which is more efficient for the community detection in large complex networks based on the nature of overlapping cliques. This algorithm does not require any priori knowledge about the number or the original division of the communities.With respect to practical applications,ComTector is challenging with five different types of networks including the classic Zachary Karate Club,Scientific Collaboration Network,South Florida Free Word Association Network,Urban Traffic Network,North America Power Grid and the Telecommunication Call Network.Experimental results show that our algorithm can discover meaningful communities that meet both the objective basis and our intuitions.
With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in physics, sociology, computer society, etc. Although this investigation of community structures has motivated many diverse algorithms, most of them are unsuitable when dealing with large networks due to their computational costs.In this paper, we present a faster algorithm ComTector, which is more efficient for the community detection in large complex networks based on the nature of overlapping cliques. This algorithm does not require any priori knowledge about the number or the original division of the communities. If respect to practical applications, ComTector is challenging with five different types of networks including the classic Zachary Karate Club, Scientific Collaboration Network, South Florida Free Word Association Network, Urban Traffic Network, North America Power Grid and the Telecommunication Call Network. Exp erimental results show that our algorithm can discover meaningful communities that meet both the objective basis and our intuitions.