Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier
This work addresses a key challenge in network science for researchers and practitioners who need to model real-world networks, though it appears incremental as it builds on existing generative models and machine learning techniques.
The paper tackles the problem of selecting the best generative model for synthesizing artificial networks that match the structural features of a given real network, and proposes GMSCN, a decision tree-based method that outperforms existing approaches in accuracy, scalability, and size-independence.
Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named "Generative Model Selection for Complex Networks" (GMSCN), outperforms existing methods with respect to accuracy, scalability and size-independence.