A Clustering Method For Complex Networks Based On The Cut Distance

In recent years,revealing the underlying similarity of various directed complex networks has became a central and interdisciplinary topic with a plethora of relevant application domains.The essence of the similarity here is that network features of the same variety of networks are highly similar while on the contrary,present low similarity when they across different kinds of networks.In this paper,we will introduce and explore a new method for clustering various complex networks based on the cut distance.We will show a correspondence between the cut distance and the similarity of two networks.This correspondence allows us to consider a broad range of complex networks and explicitly cluster various networks with high accuracy.Various machine learning technologies such as genetic algorithms is employed during the clustering process.