Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
This addresses a gap in statistical modeling for sparse networks, enabling inference in models that capture a broader range of power law exponents, though it is incremental in extending existing model classes.
The paper tackled the problem of modeling sparse networks with heavy-tailed degree distributions, particularly power laws with exponents greater than two, which existing exchangeable models cannot generate. It designed and implemented inference algorithms for a new class of non-exchangeable models, making a large class of previously intractable models useful for statistical inference.
Empirical evidence suggests that heavy-tailed degree distributions occurring in many real networks are well-approximated by power laws with exponents $η$ that may take values either less than and greater than two. Models based on various forms of exchangeability are able to capture power laws with $η< 2$, and admit tractable inference algorithms; we draw on previous results to show that $η> 2$ cannot be generated by the forms of exchangeability used in existing random graph models. Preferential attachment models generate power law exponents greater than two, but have been of limited use as statistical models due to the inherent difficulty of performing inference in non-exchangeable models. Motivated by this gap, we design and implement inference algorithms for a recently proposed class of models that generates $η$ of all possible values. We show that although they are not exchangeable, these models have probabilistic structure amenable to inference. Our methods make a large class of previously intractable models useful for statistical inference.