A Geometric Chung Lu model and the Drosophila Medulla connectome

TL;DR

This paper introduces a generalized geometric Chung-Lu model that better captures the complex, distance-dependent connectivity patterns observed in real-world graphs, demonstrated through application to the Drosophila Medulla connectome.

Contribution

It develops a new geometric Chung-Lu model that preserves key graph properties and applies it to biological neural network data.

Findings

Model accurately captures distance-dependent connectivity.

Successfully applied to Drosophila Medulla connectome.

Enhances understanding of complex network structures.

Abstract

Many real world graphs have edges correlated to the distance between them, but, in an inhomogeneous manner. While the Chung-Lu model and the geometric random graph models both are elegant in their simplicity, they are insufficient to capture the complexity of these networks. In this paper, we develop a generalized geometric random graph model that preserves many graph theoretic aspects of these real world networks. We test the validity of this model on a graphical representation of the Drosophila Medulla connectome.