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Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters (2008)
| Citations: | 208 - 17 self |
BibTeX
@MISC{Leskovec08communitystructure,
author = {Jure Leskovec and Kevin J. Lang and Anirban Dasgupta and Michael W. Mahoney},
title = {Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters},
year = {2008}
}
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Abstract
A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins with the premise that a community or a cluster should be thought of as a set of nodes that has more and/or better connections between its members than to the remainder of the network. In this paper, we explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. Rather than defining a procedure to extract sets of nodes from a graph and then attempt to interpret these sets as a “real ” communities, we employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the “best ” possible community—according to the conductance measure—over a wide range of size scales. We study over 100 large real-world networks, ranging from traditional and on-line social networks, to technological and information networks and
Keyphrases
large network large well-defined cluster community structure information network natural cluster size network community profile plot structural property real community large body large real-world network on-line social network possible community novel perspective several question conductance measure size scale wide range social entity approximation algorithm several striking conclusion meaningful community
