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Mesoscale structures are an integral part of the abstraction and analysis ofcomplex systems. They reveal a node's function in the network, and facilitateour understanding of the network dynamics. For example, they can representcommunities in social or citation networks, roles in corporate interactions, orcore-periphery structures in transportation networks. We usually detectmesoscale structures under the assumption of independence of interactions.Still, in many cases, the interactions invalidate this assumption by occurringin a specific order. Such patterns emerge in pathway data; to capture them, wehave to model the dependencies between interactions using higher-order networkmodels. However, the detection of mesoscale structures in higher-order networksis still under-researched. In this work, we derive a Bayesian approach thatsimultaneously models the optimal partitioning of nodes in groups and theoptimal higher-order network dynamics between the groups. In synthetic data wedemonstrate that our method can recover both standard proximity-basedcommunities and role-based groupings of nodes. In synthetic and real world datawe show that it can compete with baseline techniques, while additionallyproviding interpretable abstractions of network dynamics.

Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs