Com_LocalShell - Local method for detecting communities...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Local method for detecting communities James P. Bagrow 1 and Erik M. Bollt 2,1 1 Department of Physics, Clarkson University, Potsdam, New York 13699-5820, USA 2 Department of Math and Computer Science, Clarkson University, Potsdam, New York 13699-5815, USA s Received 24 December 2004; published 10 October 2005 d We propose a method of community detection that is computationally inexpensive and possesses physical significance to a member of a social network. This method is unlike many divisive and agglomerative tech- niques and is local in the sense that a community can be detected within a network without requiring knowl- edge of the entire network. A global application of this method is also introduced. Several artificial and real-world networks, including the famous Zachary karate club, are analyzed. DOI: 10.1103/PhysRevE.72.046108 PACS number s s d : 89.75.Hc, 05.10. 2 a, 87.23.Ge, 89.20.Hh I. INTRODUCTION It has been shown in the past that many interesting sys- tems can be represented as networks composed of vertices and edges f 1–4 g . Such systems include the Internet f 5 g , so- cial and friendship networks f 6 g , food webs f 7 g , and citation networks f 8,9 g . For example, a social network may represent people as vertices and edges linking vertices when those people are on a first-name basis. A topic of current interest in the area of networks has been the idea of communities and their detection. A commu- nity could be loosely described as a collection of vertices within a graph that are densely connected amongst them- selves while being loosely connected to the rest of the graph f 10–12 g . Many networks exhibit such a community structure and this motivates our work. This description, however, is somewhat vague and open to interpretation. This leads to the possibility that different techniques for detecting these com- munities may lead to slightly different yet equally valid re- sults. We emphasize this variation in Sec. II D. Several techniques have been proposed to detect commu- nity structure inside of a network. The recent and highly successful betweenness centrality algorithm due to Newman and Girvan f 13–15 g performs well within a variety of net- works but it is costly to compute f O s n 2 m d on a graph with n vertices and m edges g f 15 g . More importantly, while be- tweenness centrality has been shown to be a useful quantity for detecting community structure, it is knowledge not usu- ally attainable to a vertex within the graph . In this paper we ask, if a person were to move to a new town, what actions would he or she take to see what com- munity or communities they belong to? Most community detection methods using hierarchical clustering fall within two categories: divisive and agglomerative f 6,15 g . Both forms, including those using betweenness and other methods, are global algorithms and do not represent feasible actions that a member of a network could undertake to identify the network’s community structure. The method proposed herenetwork’s community structure....
View Full Document

This note was uploaded on 05/20/2011 for the course CAP 5515 taught by Professor Ungor during the Spring '08 term at University of Florida.

Page1 / 10

Com_LocalShell - Local method for detecting communities...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online