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- W1592937853 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." @default.
- W1592937853 created "2016-06-24" @default.
- W1592937853 creator A5060165452 @default.
- W1592937853 date "2010-01-01" @default.
- W1592937853 modified "2023-09-25" @default.
- W1592937853 title "Fuzzy Algorithm Based on Diffusion Maps for Network Partition" @default.
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- W1592937853 doi "https://doi.org/10.1007/978-3-642-14932-0_21" @default.
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