Social Network Simulation Part I: Model Selection for Realistic Social Network Generation

Nowadays, social networks appear in different forms in various domains. Online social networks, mobile communication networks, co-authoring networks and human interactions in real life are some examples of social networks. Today, analysis of dynamics and evolution of social networks is an important field of research. In this area, computer simulation is become a powerful method and Agent Based Social Simulation (ABSS) is the dominant method. In many social network simulation applications, the actual network is not available. For example, when we want to simulate the influences of the friend opinion in a friendship network, the graph of the real friendship network in the society is probably missing. In this situation, one of the prerequisites for simulating social networks is the generation of artificial networks that are similar to real social networks. Real networks exhibit nontrivial topological features (such as heavy-tailed degree distribution, high clustering, and small-world property) that distinguish them from random graphs. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. In this context, an important research problem is to identify the generative model that best fits to a target network. 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 existing generative models, we have utilized machine learning methods to develop rules for model selection. Our proposed methods outperform existing methods with respect to accuracy and scalability.  

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