Source-linked AI summary
Contact-based Social Contagion in Multiplex Networks
E. Cozzo, R. A. Baños, S. Meloni, Y. Moreno
TL;DR
Social contagion in systems spanning multiple communication platforms requires a multiplex-network treatment rather than a single aggregated graph. The paper proposes a contact-based Markov-chain framework and shows that the layer with the largest contact-matrix eigenvalue determines the critical point, while aggregation can produce wrong conclusions.
Problem
Social contagion is shaped by multiple communication platforms and network layers, but modeling an inherently multiplex system through an aggregated network may be inaccurate.
Method
The paper develops a contact-based Markov-chain framework for epidemic-like social contagion in multiplex networks, using layer contact matrices and interlayer coupling.
Results
The multiplex critical point is determined by the layer whose contact probability matrix has the largest eigenvalue, while coupling lowers non-dominant-layer thresholds and aggregation can yield wrong conclusions.
Takeaways & Limitations
A layer’s topological strength and activity jointly determine its contagion competitiveness, so critical-regime analysis requires information from all layers.
Abstract
from arXiv · showhide
We develop a theoretical framework for the study of epidemic-like social contagion in large scale social systems. We consider the most general setting in which different communication platforms or categories form multiplex networks. Specifically, we propose a contact-based information spreading model, and show that the critical point of the multiplex system associated to the active phase is determined by the layer whose contact probability matrix has the largest eigenvalue. The framework is applied to a number of different situations, including a real multiplex system. Finally, we also show that when the system through which information is disseminating is inherently multiplex, working with the graph that results from the aggregation of the different layers is flawed.