@article{Clauset:2004p5519,
title = {Finding community structure in very large networks},
author = {Aaron Clauset and Mark E. J Newman and Cristopher Moore},
journal = {arXiv},
month = {Aug},
note = {Published in: Phys. Rev. E 70, 066111 (2004)},
url = {http://arxiv.org/abs/cond-mat/0408187v2},
volume = {cond-mat.stat-mech},
year = {2004},
description = {March 2008},
abstract = { The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in which case our algorithm runs in essentially linear time, O(n log^2 n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web-site of a large online retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400,000 vertices and 2 million edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers. },
date-added = {2008-03-13 10:48:52 +0100}, pmid = {cond-mat/0408187v2}, uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p5519}, date-modified = {2008-03-13 14:41:52 +0100}, rating = {0},
keywords = {cond-mat.dis-nn cond-mat.stat-mech, }
}
::