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.
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Liu, J. (2010). Fuzzy Algorithm Based on Diffusion Maps for Network Partition. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_21
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DOI: https://doi.org/10.1007/978-3-642-14932-0_21
Publisher Name: Springer, Berlin, Heidelberg
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