2018/07/09 by Benjamin Bloem-Reddy, Adam Foster, Bloem-Reddy, Benjamin +5
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Social and Information Networks (cs.SI) #cs.LG #cs.SI #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1807.03113
Accepted for publication in the proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) 2018
arxiv created 2018/07/09 · arxiv updated 2018/07/10
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.