Extracting communities from complex networks by the k-dense method
|Extracting communities from complex networks by the k-dense method|
|Author(s)||K. Saito, T. Yamada, K. Kazama|
|Published in||Communications and Computer Sciences IEICE Transactions on Fundamentals of Electronics|
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Extracting communities from complex networks by the k-dense method is a 2008 journal article by K. Saito, T. Yamada, K. Kazama and published in Communications and Computer Sciences IEICE Transactions on Fundamentals of Electronics.
To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.
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