Multistep greedy algorithm identifies community structure in real-world and computer-generated networks

TitleMultistep greedy algorithm identifies community structure in real-world and computer-generated networks
Publication TypeJournal Article
Year of Publication2008
AuthorsSchuetz P., Caflisch A.
JournalPhysical Review E: Statistical, Nonlinear, and Soft Matter Physics
Volume78
Issue2.2
Pagination026112
Date Published2008 Aug
Type of ArticleResearch Article
ISSN1539-3755
Keywordsclusterization, communities, Escherichia coli, graph, greedy algorithm, Martin Karplus, network
Abstract

We have recently introduced a multistep extension of the greedy algorithm for modularity optimization. The extension is based on the idea that merging l pairs of communities (l > 1) at each iteration prevents premature condensation into few large communities. Here, an empirical formula is presented for the choice of the step width l that generates partitions with (close to) optimal modularity for 17 real-world and 1100 computer-generated networks. Furthermore, an in-depth analysis of the communities of two real-world networks (the metabolic network of the bacterium E. coli and the graph of coappearing words in the titles of papers coauthored by Martin Karplus) provides evidence that the partition obtained by the multistep greedy algorithm is superior to the one generated by the original greedy algorithm not only with respect to modularity, but also according to objective criteria. In other words, the multistep extension of the greedy algorithm reduces the danger of getting trapped in local optima of modularity and generates more reasonable partitions.

DOI10.1103/PhysRevE.78.026112
pubindex

0101

Alternate JournalPhys. Rev. E
PubMed ID18850902
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