How Does Label Propagation Algorithm Work in Bipartite Networks?

Community detection in networks receives much attention recently. In this paper, we investigate a recently proposed community detection algorithm—label propagation algorithm (LPA), and propose a new algorithm to make it more suitable for bipartite networks. We demonstrate through five real world dat...

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Bibliographic Details
Published in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Vol. 3; pp. 5 - 8
Main Authors: Liu, Xin, Murata, Tsuyoshi
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 2009
IEEE
Series:ACM Conferences
Subjects:
Algorithm design and analysis
Applied computing > Physical sciences and engineering > Mathematics and statistics
Complex networks
Computer science
Conferences
Detection algorithms
Electronic mail
Information science
Information systems > Information retrieval
Information systems > Information storage systems > Storage architectures > Storage network architectures
Intelligent agent
Intelligent networks
Large-scale systems
Mathematics of computing > Discrete mathematics
Mathematics of computing > Discrete mathematics > Graph theory > Graph enumeration
Theory of computation > Models of computation > Concurrency
Theory of computation > Models of computation > Concurrency > Parallel computing models
ISBN:0769538010, 9780769538013
Online Access:Get full text
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Summary:Community detection in networks receives much attention recently. In this paper, we investigate a recently proposed community detection algorithm—label propagation algorithm (LPA), and propose a new algorithm to make it more suitable for bipartite networks. We demonstrate through five real world data sets that the new algorithm, while maintaining the accuracy and the near linear time complexity of the standard LPA, is more stable than the latter. More importantly, the new algorithm is readily parallelized, which implies that it is potential for real time community analysis of large-scale bipartite networks.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.217