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Noise-tolerant model selection and parameter estimation for complex networks

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  • Aliakbary, Sadegh
  • Motallebi, Sadegh
  • Rashidian, Sina
  • Habibi, Jafar
  • Movaghar, Ali

Abstract

Real networks often exhibit nontrivial topological features that do not occur in random graphs. The need for synthesizing realistic networks has resulted in development of various network models. In this paper, we address the problem of selecting and calibrating the model that best fits a given target network. The existing model fitting approaches mostly suffer from sensitivity to network perturbations, lack of the parameter estimation component, dependency on the size of the networks, and low accuracy. To overcome these limitations, we considered a broad range of network features and employed machine learning techniques such as genetic algorithms, distance metric learning, nearest neighbor classification, and artificial neural networks. Our proposed method, which is named ModelFit, outperforms the state-of-the-art baselines with respect to accuracy and noise tolerance in different network datasets.

Suggested Citation

  • Aliakbary, Sadegh & Motallebi, Sadegh & Rashidian, Sina & Habibi, Jafar & Movaghar, Ali, 2015. "Noise-tolerant model selection and parameter estimation for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 100-112.
    • Handle: RePEc:eee:phsmap:v:427:y:2015:i:c:p:100-112
    DOI: 10.1016/j.physa.2015.02.032
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    References listed on IDEAS

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    1. Lynne Hamill & Nigel Gilbert, 2009. "Social Circles: A Simple Structure for Agent-Based Social Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-3.
    2. Volchenkov, D & Blanchard, Ph, 2002. "An algorithm generating random graphs with power law degree distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 315(3), pages 677-690.
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    1. Attar, Niousha & Aliakbary, Sadegh & Nezhad, Zahra Hosseini, 2020. "Automatic generation of adaptive network models based on similarity to the desired complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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