Fuzzy algorithm based on diffusion maps for network partition

Authors:
Jian Liu
Affiliations:
LMAM and School of Mathematical Sciences, Peking University, Beijing, P.R. China
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

To find the best partition of a large and complex network into a small number of communities has been addressed in many different ways. The method conducted in k-means form under the framework of diffusion maps and coarse-grained random walk is implemented for graph partitioning, dimensionality reduction and data set parameterization. In this paper we extend this framework to a probabilistic setting, in which each node has a certain probability of belonging to a certain community. The algorithm (FDM) for such a fuzzy network partition is presented and tested, which can be considered as an extension of the fuzzy c- means algorithm in statistics to network partitioning. Application to three representative examples is discussed.