The study of complex systems from a network perspective has undoubtedly been a success story so far. Innumerable studies have shown how schematizing the elements and their interactions of a complex system with nodes and links can help us to provide insights into the system's structure, dynamics, and function. The increasing availability of data on complex systems creates a great opportunity for this approach to further prosper. That is, we anticipate that more and more systems can and will be studied with tools from network science.
However, the fact that the data are increasingly rich and complex also give rise to new and unique challenges: Studies of time-varying complex systems facilitated by newly available high-resolution longitudinal data question the effectiveness of the conventional network approach and suggest that higher-order models are required to gain insight into the structure and dynamics of such systems. For similar reasons, models enriched by node activities or temporal motifs have been suggested. Moreover, increasingly available rich data on static networks have stimulated work on augmented network models that incorporate multiple layers, modules, or link types.
All these approaches are seemingly different, yet they all highlight the same thing: For a number of complex systems, a simple abstraction of their organization into nodes and links is not sufficient for understanding their structure, dynamics, and function. This observation raises fundamental questions: When are simple network models sufficient and when are they not? What additional ingredients are needed to accurately model the dynamical processes? With access to more and more relational data, what are the most efficient ways to capture the structural information? These are questions that we would like to address in this workshop.
Being the continuation of the highly successful editions held in 2014, 2015 and 2016, this satellite workshop will be held at NetSci 2017 in Indianapolis. We are proud to host a great line-up of speakers who will report on the latest advance in higher-order network modeling techniques.