Propagation patterns of contagion processes on networks and hypernetworks
Propagation patterns of contagion processes on networks and hypernetworks
Abstract
Contagion processes, representing the spread of infectious diseases, information, or social behaviors, are often schematized as taking place on networks, which encode for instance the interactions between individuals. This general picture hides a variety of situations: such processes can be represented as simple contagions, i.e., as a contagion process involving one connection at a time, or as complex contagions, in which multiple interactions are needed for a contagion event. Each type of contagion can moreover correspond to a variety of models, and some processes also include group effects, which cannot be represented within the network framework and require the use of hypergraphs to encode interactions in groups of any size.
While the impact of the structure of the interactions on spreading processes has been widely investigated in many of the corresponding models, it is not the case of the reverse question: do different processes unfolding on a given network lead to different infection patterns? How do the infection patterns depend on a model’s parameters or on the nature of the contagion processes? Can we deduce the nature of the contagion process from the observation of a propagation? I will first address these issue by investigating the average infection patterns for a variety of models of simple and complex contagion with different mechanisms. The results show that, for simple contagion models, the infection patterns are extremely robust across models and parameters, meaning that it is possible to study crucial features of a spread from schematized models. For complex contagion models, non-trivial dependencies on models parameters emerge, but the patterns remain rather similar and robust. In a second part, I investigate whether the residual differences between the patterns make it possible to determine, from the observation of a single instance of a spreading process, whether it is due to a simple or a complex contagion process, and whether it involves group effects. Using the observation of the order in which network nodes are infected, and its correlations with their local topology, I will show how to build a method using only limited information to distinguish between there several possible contagion mechanisms.
Online (Zoom)
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Short Bio
Alain Barrat is senior researcher at the Center for Theoretical Physics in Marseilles, France. He has also been research scientist at the Institute for Scientific Interchange in Turin, Italy from 2006 to 2019 and Specially Appointed Professor at the Tokyo Tech World Research Hub Initiative (Tokyo, Japan) from 2019 to 2022. From 2014 to 2020, he has been vice-president treasurer of the Complex Systems Society. He currently serves as Divisional Associate Editor of Physical Review Letters, and as Editor of JSTAT and of Advances in Complex Systems.
His research focuses on complex networks and interdisciplinary aspects of statistical physics. He is co-author of the book “Dynamical processes on complex networks”, and co-founder of the SocioPatterns collaboration, which focuses on temporal networks of contacts between individuals, from the data collection to their analysis and use in fields ranging from epidemiology to social sciences.