Inference for growing trees

TL;DR

This paper develops exact and efficient inference methods for growing trees, enabling better analysis of network evolution, history reconstruction, and model selection with practical applications.

Contribution

It introduces novel inference techniques specifically designed for growing trees, improving accuracy and efficiency over existing methods.

Findings

Exact inference methods for growing trees are feasible and efficient.

Applications include network interpolation, history reconstruction, and model fitting.

The methods outperform previous approaches in accuracy and computational speed.

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.