Finding community structure in very large networks.
TLDR
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.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 (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.read more
Citations
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Posted Content
Parallel Heuristics for Scalable Community Detection
TL;DR: In this article, a parallelization heuristic for fast community detection using the Louvain method as the serial template is presented, which is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities.
Proceedings ArticleDOI
Community detection using Ant Colony Optimization
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Topics in dynamic research communities: An exploratory study for the field of information retrieval
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