Inference for growing trees
Social and Information Networks
2021-01-27 v2 Physics and Society
Abstract
One can often make inferences about a growing network from its current state alone. For example, it is generally possible to determine how a network changed over time or pick among plausible mechanisms explaining its growth. In practice, however, the extent to which such problems can be solved is limited by existing techniques, which are often inexact, inefficient, or both. In this article we derive exact and efficient inference methods for growing trees and demonstrate them in a series of applications: network interpolation, history reconstruction, model fitting, and model selection.
Cite
@article{arxiv.1910.04788,
title = {Inference for growing trees},
author = {George T. Cantwell and Guillaume St-Onge and Jean-Gabriel Young},
journal= {arXiv preprint arXiv:1910.04788},
year = {2021}
}