Multiplex Networks with Intrinsic Fitness: Modeling the Merit-Fame Interplay via Latent Layers

TL;DR

This paper introduces a novel multiplex network growth model combining intrinsic fitness and preferential attachment, providing analytical solutions that address previous homogeneity limitations in network modeling.

Contribution

The paper presents a new multiplex network model with heterogeneous fitness and connection patterns, along with analytical solutions for degree and fitness distributions.

Findings

Analytical closed-form solutions for joint fitness-degree distributions.

Model addresses homogeneity issues in existing multiplex network models.

Provides insights into the interplay between merit and fame in network growth.

Abstract

We consider the problem of growing multiplex networks with intrinsic fitness and inter-layer coupling. The model comprises two layers; one that incorporates fitness and another in which attachments are preferential. In the first layer, attachment probabilities are proportional to fitness values, and in the second layer, proportional to the sum of degrees in both layers. We provide analytical closed-form solutions for the joint distributions of fitness and degrees. We also derive closed-form expressions for the expected value of the degree as a function of fitness. The model alleviates two shortcomings that are present in the current models of growing multiplex networks: homogeneity of connections, and homogeneity of fitness. In this paper, we posit and analyze a growth model that is heterogeneous in both senses.