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Model Selection for Complex Network Generation

Motallebi, Sadegh | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44793 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Habibi, Jafar
  7. Abstract:
  8. Nowadays, there exist many real networks with distinctive features in comparison with random networks. Social networks, collaboration networks, citation networks, protein networks and communication networks are some example of complex network classes. Nowadays these networks are widespread and have many applications and the study of complex networks is an important research area. In many applications, the “synthetic networks generation” is one of the first levels of complex networks analysis. This level has many applications such as simulation and extrapolation. Many generative models are proposed for complex network modeling in recent years. By the use of these models, synthetic networks similar to real networks are generated that are helpful for the study of complex networks. Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering and small-worldness. The aim of generative models is to generate artificial networks similar to given real networks. In this situation, selection of the proper model among existing models for different target networks is an important research problem. The result of this project is the selection of the proper model according to different topological features that best fits to a target network automatically. 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
  9. Keywords:
  10. Complex Network ; Generating Model ; Synthetic Networks ; Model Selection ; Network Structural Features ; Decision Tree Learning ; Machine Learning ; Social Networks

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