@article{clauset2004,
  abstract = {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(mdlog 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 log2 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.},
  author = {Clauset, Aaron and Newman, M. E. J. and and Cristopher Moore},
  doi = {10.1103/PhysRevE.70.066111},
  interhash = {69be2649d5ff3e66ad7dfadac4a1841f},
  intrahash = {458e03efb1ef50a5338907bb58c426f6},
  journal = {Physical Review E},
  pages = {1-- 6},
  title = {Finding community structure in very large networks},
  year = 2004
}