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Real networks exhibit nontrivial topological features such as heavy-taileddegree distribution, high clustering, and small-worldness. Researchers havedeveloped several generative models for synthesizing artificial networks thatare structurally similar to real networks. An important research problem is toidentify the generative model that best fits to a target network. In thispaper, we investigate this problem and our goal is to select the model that isable to generate graphs similar to a given network instance. By the means ofgenerating synthetic networks with seven outstanding generative models, we haveutilized machine learning methods to develop a decision tree for modelselection. Our proposed method, which is named "Generative Model Selection forComplex Networks" (GMSCN), outperforms existing methods with respect toaccuracy, scalability and size-independence.

Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier