The Workshop will host an interdisciplinary mix of leading researchers in the field of higher-order networks, exposing novel and updated tools by which networks beyond pairwise interactions are modeled and utilized in various applications.

The Workshop’s principal aim will be to create connections between the presented results and relevant projects that are currently of interest for the Complexity Science Hub (CSH), especially those involving data analysis and social networks, and to foster a series of collaborations between the local staff at CSH and the invited speakers.  The tentative plan is to alternate between talks and plenty of discussion within the two days of the event.

Complex network science was born 25 years ago as a multidisciplinary application of graph theory, and it has produced major results in many areas. Later, it expanded to encompass systems consisting of separate layers of individual networks, possibly with common nodes, producing new, unexpected insights and sparking a second wave of discoveries in complexity science. However, much more recently, the limitations of this paradigm became clear when researchers noted that using graphs as the underlying structures, one can only model interactions between pairs of elements, whereas collective properties of many real-world systems are due to interactions occurring over larger sets of units.

To address this issue, scientists have begun using the mathematical structure of hypergraphs, in which links can occur between more than two nodes at the same time. The resulting hyper-networks can thus naturally represent many-body interactions, allowing one to describe systems with a much higher degree of precision. This shift of paradigm has stimulated a new movement of interdisciplinary research that uses a synergy of graph theory, dynamical systems theory, control theory, and numerous further disciplines in mathematical and statistical physics, which has already led to unexpected results in a broad range of areas.

Participation is by invitation only.