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In this article, the authors use the [[greedy algorithm]] to coarse-grain the network. However, for large-scale networks, this algorithm is very inefficient. Subsequently, Griebenow et al. <ref>Griebenow R, Klein B, Hoel E. Finding the right scale of a network: efficient identification of causal emergence through spectral clustering[J]. arXiv preprint arXiv:190807565, 2019.</ref> proposed a method based on [[spectral clustering]] to identify causal emergence in [[preferential attachment networks]]. Compared with the [[greedy algorithm]] and the [[gradient descent algorithm]], the [[spectral clustering algorithm]] has less computation time and the causal emergence of the found macroscopic network is also more significant.

In this article, the authors use the [[greedy algorithm]] to coarse-grain the network. However, for large-scale networks, this algorithm is very inefficient. Subsequently, Griebenow et al. <ref>Griebenow R, Klein B, Hoel E. Finding the right scale of a network: efficient identification of causal emergence through spectral clustering[J]. arXiv preprint arXiv:190807565, 2019.</ref> proposed a method based on [[spectral clustering]] to identify causal emergence in [[preferential attachment networks]]. Compared with the [[greedy algorithm]] and the [[gradient descent algorithm]], the [[spectral clustering]] algorithm has less computation time and the causal emergence of the found macroscopic network is also more significant.

 

===Application on biological networks===

===Application on biological networks===