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Data-driven analysis of complex networks has been in the focus of researchfor decades. An important area of research is to study how well real networkscan be described with a small selection of metrics, furthermore how wellnetwork models can capture the relations between graph metrics observed in realnetworks. In this paper, we apply machine learning techniques to investigatethe aforementioned problems. We study 500 real-world networks along with 2,000synthetic networks generated by four frequently used network models withpreviously calibrated parameters to make the generated graphs as similar to thereal networks as possible. This paper unifies several branches of data-drivencomplex network analysis, such as the study of graph metrics and theirpair-wise relationships, network similarity estimation, model calibration, andgraph classification. We find that the correlation profiles of the structuralmeasures significantly differ across network domains and the domain can beefficiently determined using a small selection of graph metrics. The structuralproperties of the network models with fixed parameters are robust enough toperform parameter calibration. The goodness-of-fit of the network models highlydepends on the network domain. By solving classification problems, we find thatthe models lack the capability of generating a graph with a high clusteringcoefficient and relatively large diameter simultaneously. On the other hand,models are able to capture exactly the degree-distribution-related metrics.

Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts