Applications of Renormalizable Binary Fitness-Based Network Models

EasyChair Preprint 5438

Abstract

A recent binary fitness-based renormalizable network models is fit to empirical networks across several levels. First, the case when a "natural" hierarchical partition is available is examined, and examplified with trade data. The goodness of fit at a fixed level is compared to the output of established fitness-based probabilistic models.

Then the goodness of fit of the renormalizable model is assessed when the hierarchical partition is generated by state-of-the art model-based hierarchical clustering methods.

Keyphrases: Clustering, fitness, hierarchical, multi-scale

@booklet{EasyChair:5438,
  author    = {Aurélien Hazan},
  title     = {Applications of Renormalizable Binary Fitness-Based Network Models},
  howpublished = {EasyChair Preprint 5438},
  year      = {EasyChair, 2021}}