Abstract
A number of recent studies have focused on the statistical properties of
networked systems such as social networks and the World-Wide Web. Researchers
have concentrated particularly on a few properties which seem to be common to
many networks: the small-world property, power-law degree distributions, and
network transitivity. In this paper, we highlight another property which is
found in many networks, the property of community structure, in which network
nodes are joined together in tightly-knit groups between which there are only
looser connections. We propose a new method for detecting such communities,
built around the idea of using centrality indices to find community boundaries.
We test our method on computer generated and real-world graphs whose community
structure is already known, and find that it detects this known structure with
high sensitivity and reliability. We also apply the method to two networks
whose community structure is not well-known - a collaboration network and a
food web - and find that it detects significant and informative community
divisions in both cases.
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