JSM 2011 Online Program

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Abstract Details

Activity Number: 585
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #302624
Title: Network Community Definition and Detection
Author(s): Shuqin Zhang*+ and Hongyu Zhao
Companies: Yale University and Yale University
Address: , New Haven, CT, 06511,
Keywords: communitiy structure ; random graph
Abstract:

Community structure or module structure is an important issue in many different kinds of networks such as social networks and biological networks. Several types of algorithms for identifying the community structure have been proposed which include the clustering techniques, modularity optimization, spectral partitioning, k-clique percolation and some other methods. Although all these methods have been shown to have their advantages, there is no consistent definition of the community structure. Very few theoretical results for the detection methods have been given. In this paper, we give a new definition for the network community based on the random graphs. An algorithm for detecting the community structure is also proposed. Numerical experiments based on simulations and real data are given to demonstrate the efficiencies of the algorithm. Theoretical analyses of the definition and the algorithm are also given to explain and support the numerical results.


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