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Finding community structure in very large networks

Bibliographic Data

ID23319954
AuthorsAaron Clauset (0000-0002-3529-8746, University of New Mexico), M E J Newman (0000-0002-0907-1660, University of Michigan), Cristopher Moore (0000-0002-2062-1942, University of New Mexico)
Year2004
Volume70
Issue6
Pages066111-066111
Publication date2004-12-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePhysical Review E (JOURNAL)
Journal identifiersISSN: 1539-3755 • E-ISSN: 1550-2376
PublisherAmerican Physical Society (APS) (PUBLISHER)
DOI10.1103/physreve.70.066111
PMID15697438
OpenAlexW2047940964
LanguageEN
Citations received433
References cited32

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 (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately 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 on-line 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 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

Binary logarithm · Business · Combinatorics · Community structure · Complex network · Data mining · Dendrogram · Network structure · Purchasing · Sociology · World Wide Web · Complex Network Analysis Techniques · Computer Science · Marketing · Mathematics · Opinion Dynamics and Social Influence · Peer-to-Peer Network Technologies · Theoretical Computer Science

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Unique citing works433
Citations per year20,62
Citation span2005 - 2026 (22)
Citation velocitycurrent
Highly citedYes
Citation typesNeutral: 381

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