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1 results

  • Chapter 23 - Statistical models and inference

  • from Part III - Fundamentals
  • James Bagrow, University of Vermont, Yong‐Yeol Ahn, Indiana University, Bloomington
  • Book:
    Working with Network Data
    Published online:
    06 June 2024
    Print publication:
    13 June 2024, pp 351-376

  • Summary

    In this chapter, we explore several important statistical models. Statistical models allow us to perform statistical inference—the process of selecting models and making predictions about the underlying distributions—based on the data we have. Many approaches exist, from the stochastic block model and its generalizations to the edge observer model, the exponential random graph model, and the graphical LASSO. As we show in this chapter, such models help us understand our data, but using them may at times be challenging, either computationally or mathematically. For example, the model must often be specified with great care, lest it seize on a drastically unexpected network property or fall victim to degeneracy. Or the model must make implausibly strong assumptions, such as conditionally independent edges, leading us to question its applicability to our problem. Or even our data may be too large for the inference method to handle efficiently. As we discuss, the search continues for better, more tractable statistical models and more efficient, more accurate inference algorithms for network data.