A likelihood based framework for assessing network evolution models tested on real network data

TL;DR

This paper introduces a likelihood-based framework for evaluating network evolution models, validated on diverse real-world network data including internet, social media, and co-authorship networks.

Contribution

It provides a statistically rigorous method for assessing network evolution models using likelihood, applicable to various real network datasets.

Findings

Effective assessment of network models demonstrated on real data

Framework applicable to diverse network types

Provides a basis for statistically sound network analysis

Abstract

This paper presents a statistically sound method for using likelihood to assess potential models of network evolution. The method is tested on data from five real networks. Data from the internet autonomous system network, from two photo sharing sites and from a co-authorship network are tested using this framework.