Focus 

Higher-order interaction networks

Guest Edited by Prof Ginestra Bianconi (Queen Mary University) in collaboration with our Editorial Board Member Prof Federico Battiston (Central European University).

Many real-world systems, from social relationships to the human brain, can be successfully described as graphs: a collection of elementary units (nodes), and their pairwise interactions (links). Over the years, network approaches have been successfully applied to a wide class of domains, from economics to ecology. Thanks to technological advances and an increasingly interconnected world, data availability has recently exploded, amplifying the potential and applicability of network science approaches. Despite being widespread, traditional network descriptions often do not provide a faithful representation of reality. In many systems, interactions among the units are not limited to pairs, but can occur in groups of greater size. This is the case of human face-to-face interactions, species interactions in an ecosystem, or neurological coupling among different brain regions. These ‘higher-order interactions’ are better described by simplicial complexes and hypergraphs, more complex mathematical structures with respect to traditional graphs.

Building on early mathematical work on topological data analysis and graph theory, and supported by new experimental evidence, the investigation of networks with higher-order interactions has become ubiquitous in the last decade. Taking into account the higher-order structure of real-world systems has revealed new patterns of interactions and functionality which arise from inherently high-order features and could not be understood by limiting the analysis of structural properties to pairwise links. From social contagion to synchronisation, the introduction of higher-order interactions in networked systems has already been shown to give rise to new emergent physical phenomena, which cannot be predicted by breaking higher-order interactions into simple low-order dyads. 

The aim of this Collection is to provide, as a single resource, a venue for the latest and most important findings on higher-order interaction networks, which we believe will become an important reference for physicists working on the topic in the future years.

artistic representation of higher order interactions between nodes

About the Guest Editors

Federico Battiston is Associate Professor at the Department of Network and Data Science at Central European University, and the Organizer of the Central European Chapter of the Network Science Society. Since January 2020, he serves as Editorial Board Member for Communcations Physics. Before joining DNDS-CEU, he held postdoctoral positions at University College London, and at the Brain & Spine Institute in Paris. Federico holds a PhD in Applied Mathematics from Queen Mary University of London, and degrees in Theoretical Physics from Sapienza University of Rome. He works on the structure and dynamics of complex networks, on network neuroscience, and on computational social science. 

Ginestra Bianconi is Professor of Applied Mathematics in the School of Mathematical Sciences of Queen Mary University of London and she is  Alan Turing Fellow at the Alan Turing Institute. Currently she is Chief Editor of JPhys Complexity, Editor of PloSOne, and Scientific Reports, and she is Associate Editor of Chaos, Solitons and Fractals. Her research activity on Statistical Mechanics and Network Science includes Network Theory and its interdisciplinary applications. She has formulated the Bianconi-Barabasi model that displays the Bose-Einstein condensation in complex networks. She  has worked in network entropy and network ensembles and on dynamical processes on networks. In the last years she has been focusing on multilayer networks, simplicial complex  geometry and topology, percolation and network control. She is the author of the book Multilayer Networks: Structure and Function  by Oxford University Press.