Assortativity in geometric and scale-free networks

TL;DR

This paper critically examines how assortativity is measured in networks, reveals limitations of common metrics like Pearson's coefficient especially in heavy-tailed degree distributions, and proposes a new model with tunable assortativity.

Contribution

It rigorously proves the inadequacy of Pearson's assortativity coefficient for heavy-tailed networks and introduces an extended GIRG model with adjustable assortativity.

Findings

Pearson's coefficient fails to measure assortativity in heavy-tailed networks.

Real-world networks often show non-neutral assortativity, unlike generative models.

The extended GIRG model allows for controlled assortativity levels.

Abstract

The assortative behavior of a network is the tendency of similar (or dissimilar) nodes to connect to each other. This tendency can have an influence on various properties of the network, such as its robustness or the dynamics of spreading processes. In this paper, we study degree assortativity both in real-world networks and in several generative models for networks with heavy-tailed degree distribution based on latent spaces. In particular, we study Chung-Lu Graphs and Geometric Inhomogeneous Random Graphs (GIRGs). Previous research on assortativity has primarily focused on measuring the degree assortativity in real-world networks using the Pearson assortativity coefficient, despite reservations against this coefficient. We rigorously confirm these reservations by mathematically proving that the Pearson assortativity coefficient does not measure assortativity in any network with sufficiently heavy-tailed degree distributions, which is typical for real-world networks. Moreover, we find that other single-valued assortativity coefficients also do not sufficiently capture the wiring preferences of nodes, which often vary greatly by node degree. We therefore take a more fine-grained approach, analyzing a wide range of conditional and joint weight and degree distributions of connected nodes, both numerically in real-world networks and mathematically in the generative graph models. We provide several methods of visualizing the results. We show that the generative models are assortativity-neutral, while many real-world networks are not. Therefore, we also propose an extension of the GIRG model which retains the manifold desirable properties induced by the degree distribution and the latent space, but also exhibits tunable assortativity. We analyze the resulting model mathematically, and give a fine-grained quantification of its assortativity.