![]() ![]() The core of network comparison is to define an effective dissimilarity metric 18, 19, 20, which can capture and adequately quantify topological differences between networks even when they have different sizes. Network comparison is the basic of many network analysis applications such as model selection 14, network classification and clustering 15, anomaly and discontinuity detection 16, and evaluation of sampling algorithms 17. Therefore, how to accurately extract network topological characteristics and find out the general rules of different systems is the focus and difficulty of network science 9, 10, 11.Ībout network topologies, many scholars have shown great interest in comparison of complex networks 1, 2, 12, 13, which is mainly to measure the differences between two networks by comparing their topological properties. The most representative is the study about the nontrivial topological properties such as community structure and long-tail degree distribution. ![]() One of the most important features of network science is that it can extract the common characteristics of different systems under the network representation. Since various systems with complex interactions can be abstractly represented as networks, network science has developed rapidly and widely used in various fields such as biology 1, 2, 3, economics 4, 5 and social science 6, 7, 8.
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