Contagion Dynamics on Higher-Order Networks

Understanding the dissemination of diseases, information, and behavior standsas a paramount research challenge in contemporary network and complex systemsscience. The COVID-19 pandemic and the proliferation of misinformation arerelevant examples of the importance of these dynamic processes, which haverecently gained more attention due to the potential of higher-order networks tounlock new avenues for their investigation. Despite being in its early stages,the examination of social contagion in higher-order networks has witnessed asurge of novel research and concepts, revealing different functional forms forthe spreading dynamics and offering novel insights. This review presents afocused overview of this body of literature and proposes a unified formalismthat covers most of these forms. The goal is to underscore the similarities anddistinctions among various models, to motivate further research on the generaland universal properties of such models. We also highlight that while the pathfor additional theoretical exploration appears clear, the empirical validationof these models through data or experiments remains scant, with an unsettledroadmap as of today. We therefore conclude with some perspectives aimed atproviding possible research directions that could contribute to a betterunderstanding of this class of dynamical processes, both from a theoretical anda data-oriented point of view.