Information Diffusion on Complex Networks

A plethora of real-world processes is driven by the spread of information throughout a system. This diversity has led to the study of diffusion processes from different perspectives and by different scientific domains. While these domains share a core concept, their respective communities act mostly autonomously, not profiting from each other's insights. In particular, the following areas analyze related aspects in diffusion processes: first-passage percolation, stochastic infection processes, evolutionary dynamics, temporal graphs, and certain parts of game theory. These areas study the dynamics of (random) processes on networks where vertices switch between a discrete number of states, driven by the states of their neighbors. Some results in these domains are derived with similar methods and can be translated into each other, but the tools and models complement each other. Strikingly, all results are limited with respect to at least one property of their model, but these limiting aspects differ between the fields.

Workshop Aims

This workshop brings together researchers from all of the domains above.

We aim to:

- exchange knowledge from the different domains, allowing each participant to know the state of the art of the other domains and to interpret key results;
- establish joint notation and terminology;
- initiate cross-domain collaboration;
- conduct preliminary research on open problems in diverse groups.