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- W2376618091 abstract "Community structure is an important feature of networks, and the correct detection of communities is a fundamental problem in network analysis. Statistical inference has recently been proposed for successful detection, provided the number of communities can be appropriately estimated a priori. In the absence of such information, model selection by determination of the number of communities remains an issue. We show here that correlation between communities from a highly parceled partition can be used to estimate a narrow range of variation for the real number of communities. This range, further elaborated by modularity-based belief propagation, correctly identifies communities. Testing on synthetic networks generated by a stochastic block model and a set of real-world networks shows that our method can alleviate the effects of modularity fluctuations well and enhance the ability of community detection of the bare modularity-based belief propagation method." @default.
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- W2376618091 date "2016-05-11" @default.
- W2376618091 modified "2023-09-24" @default.
- W2376618091 title "Correlation enhanced modularity-based belief propagation method for community detection in networks" @default.
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- W2376618091 doi "https://doi.org/10.1088/1742-5468/2016/05/053301" @default.
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