Patterns of Multiplex Layer Entanglement across Real and Synthetic Networks

TL;DR

This paper studies the structural patterns of multiplex networks across various real-world systems, revealing differences between social and biological networks and proposing a generator for synthetic multiplex networks with similar properties.

Contribution

It introduces measures of layer consistency, demonstrates their effectiveness in classifying real networks, and develops a generator for synthetic multiplex networks that replicate observed patterns.

Findings

Entanglement homogeneity and intensity differentiate social and biological networks.

Real networks can be categorized based on these measures.

Synthetic networks exhibit similar multiplex patterns as real networks.

Abstract

Real world complex networks often exhibit multiplex structure, connecting entities from different aspects of physical systems such as social, transportation and biological networks. Little is known about general properties of such networks across disciplines. In this work, we first investigate how consistent are connectivity patterns across 35 real world multiplex networks. We demonstrate that entanglement homogeneity and intensity, two measures of layer consistency, indicate apparent differences between social and biological networks. We also investigate trade, co-authorship and transport networks. We show that real networks can be separated in the joint space of homogeneity and intensity, demonstrating the usefulness of the two measures for categorization of real multiplex networks. Finally, we design a multiplex network generator, where similar patterns (as observed in real networks), are emerging over the analysis of 11{,}905 synthetic multiplex networks with various topological properties.