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Extracting communities from complex networks by the k-dense method
Abstract To understand the structural and functionaTo 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. to those obtained by the k-clique method.
Abstractsub To understand the structural and functionaTo 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. to those obtained by the k-clique method.
Bibtextype article  +
Has author K. Saito + , T. Yamada + , K. Kazama +
Has remote mirror http://dx.doi.org/10.1093/ietfec/e91-a.11.3304  +
Number of citations by publication 0  +
Number of references by publication 0  +
Peer-reviewed Yes  +
Published in Communications and Computer Sciences IEICE Transactions on Fundamentals of Electronics +
Title Extracting communities from complex networks by the k-dense method +
Type journal article  +
Year 2008 +
Creation dateThis property is a special property in this wiki. 20 September 2014 17:11:55  +
Categories Publications without keywords parameter  + , Publications without language parameter  + , Publications without license parameter  + , Publications without DOI parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 20 September 2014 17:11:55  +
DateThis property is a special property in this wiki. 2008  +
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Extracting communities from complex networks by the k-dense method + Title
 

 

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