Abstract
Coarse graining of complex networks is an important method to study large-scale complex networks, and is also in the focus of network science today. This paper tries to develop a new coarse-graining method for complex networks, which is based on the node similarity index. From the information structure of the network node similarity, the coarse-grained network is extracted by defining the local similarity and the global similarity index of nodes. A large number of simulation experiments show that the proposed method can effectively reduce the size of the network, while maintaining some statistical properties of the original network to some extent. Moreover, the proposed method has low computational complexity and allows people to freely choose the size of the reduced networks.
Highlights
Many complex systems in reality can be abstracted into complex networks [1] for research
This paper tries to develop a new coarse-graining method for complex networks, which is based on the node similarity index
We have developed a new algorithm to reduce the sizes of complex networks
Summary
Many complex systems in reality can be abstracted into complex networks [1] for research. Given a complex network with N nodes and E edges, which is considerably large and hard to be delt with, the coarse-graining technique aims at mapping the large network into a mesoscale network, while preserving some topological or dynamic properties of the original network. This strategy is based on the idea of clustering nodes with similar or same nature together. Referring to literature [18], the network reduction is related to segmenting the central nodes by implementing the k-means clustering techniques, etc These methods can well maintain some of the original networks.
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