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In this paper, we propose a new efficient algorithm which makes a good thing out of the betweenness centrality and the local information to detect community structures in complex networks. When being tested on some typical real world networks, our algorithm demonstrates excellent community partition results and very fast processing performance, much faster than the existing classical community detecting...
Many complex networks possess the scale-free property, which makes the task of detecting communities from these networks difficult. The application of traditional clustering algorithms on these networks has not yielded a great deal of success. In this paper we present a method of detecting community structure based on hypergraph model to address this problem. The hypergraph model maps the relationship...
Over the past decade, community structure, a statistical property of networked systems such as social network and World Wide Web, has attracted considerable attention in data mining field because it enables description and prediction of complex networks. Many highly sensitive graph clustering algorithms were developed for identification of communities having dense connections internally and loose...
Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number...
Data clustering is an important technique to extract and understand relevant information in large data sets. In this paper, a clustering algorithm based on graph theoretic models and community detection in complex networks is proposed. Two steps are involved in this processing: The first step is to represent input data as a network and the second one is to partition the network into subnetworks producing...
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