%0 Generic %1 citeulike:95936 %A Clauset, Aaron %A Newman, M. E. J. %A Moore, Cristopher %D 2004 %K clustering large network community %T Finding community structure in very large networks %U http://arxiv.org/abs/cond-mat/0408187 %X 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.

@misc{citeulike:95936, 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.}, added-at = {2006-02-15T08:00:29.000+0100}, author = {Clauset, Aaron and Newman, M. E. J. and Moore, Cristopher}, biburl = {https://www.bibsonomy.org/bibtex/2f9a12630a6d31d576ea5222219a4cf0b/hotho}, citeulike-article-id = {95936}, eprint = {cond-mat/0408187}, interhash = {2c68e3c981a00380692a3b0b661d7cfd}, intrahash = {f9a12630a6d31d576ea5222219a4cf0b}, keywords = {clustering large network community}, month = {August}, priority = {0}, timestamp = {2006-02-15T08:00:29.000+0100}, title = {Finding community structure in very large networks}, url = {http://arxiv.org/abs/cond-mat/0408187}, year = 2004 }