  Mesoscale structures are an integral part of the abstraction and analysis of
complex systems. They reveal a node's function in the network, and facilitate
our understanding of the network dynamics. For example, they can represent
communities in social or citation networks, roles in corporate interactions, or
core-periphery structures in transportation networks. We usually detect
mesoscale structures under the assumption of independence of interactions.
Still, in many cases, the interactions invalidate this assumption by occurring
in a specific order. Such patterns emerge in pathway data; to capture them, we
have to model the dependencies between interactions using higher-order network
models. However, the detection of mesoscale structures in higher-order networks
is still under-researched. In this work, we derive a Bayesian approach that
simultaneously models the optimal partitioning of nodes in groups and the
optimal higher-order network dynamics between the groups. In synthetic data we
demonstrate that our method can recover both standard proximity-based
communities and role-based groupings of nodes. In synthetic and real world data
we show that it can compete with baseline techniques, while additionally
providing interpretable abstractions of network dynamics.
