Autors:

CiteWeb id: 20040000139

CiteWeb score: 3548

DOI: 10.1103/PhysRevE.70.066111

: 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.

The publication "Finding community structure in very large networks" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Physics it is included to the Top 1000. Additionally, the publicaiton "Finding community structure in very large networks" is placed in the Top 1000 among other scientific works published in 2004.
Links to full text of the publication: