Evaluating Network Models: A Likelihood Analysis

TL;DR

This paper introduces a likelihood-based evaluation method for network models, demonstrating its effectiveness on real Internet data and showing it can identify better-fitting models and optimal parameters.

Contribution

A unified likelihood comparison approach for evaluating network models, improving model selection and parameter tuning based on real network data.

Findings

GLP model outperforms Tang model in likelihood.

Both GLP and Tang models outperform BA and ER models.

Likelihood-based parameters better capture network evolution features.

Abstract

Many models are put forward to mimic the evolution of real networked systems. A well-accepted way to judge the validity is to compare the modeling results with real networks subject to several structural features. Even for a specific real network, we cannot fairly evaluate the goodness of different models since there are too many structural features while there is no criterion to select and assign weights on them. Motivated by the studies on link prediction algorithms, we propose a unified method to evaluate the network models via the comparison of the likelihoods of the currently observed network driven by different models, with an assumption that the higher the likelihood is, the better the model is. We test our method on the real Internet at the Autonomous System (AS) level, and the results suggest that the Generalized Linear Preferential (GLP) model outperforms the Tel Aviv Network Generator (Tang), while both two models are better than the Barab\'asi-Albert (BA) and Erd\"os-R\'enyi (ER) models. Our method can be further applied in determining the optimal values of parameters that correspond to the maximal likelihood. Experiment indicates that the parameters obtained by our method can better capture the characters of newly-added nodes and links in the AS-level Internet than the original methods in the literature.