# Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs

arXiv: https://arxiv.org/abs/2301.11120 · HTML: https://mlsanity.com/paper/2301.11120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [The ground truth about metadata and community detection in networks](https://mlsanity.com/paper/1608.05878/) (2016)
- [Clustering and Community Detection in Directed Networks: A Survey](https://mlsanity.com/paper/1308.0971/) (2013)
- [Memory in network flows and its effects on spreading dynamics and community detection](https://mlsanity.com/paper/1305.4807/) (2013)
- [Contact patterns among high school students](https://mlsanity.com/paper/1409.5318/) (2014)
- [Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers](https://mlsanity.com/paper/1409.7017/) (2014)
- [Tensor Spectral Clustering for Partitioning Higher-order Network Structures](https://mlsanity.com/paper/1502.05058/) (2015)
- [Role Discovery in Networks](https://mlsanity.com/paper/1405.7134/) (2014)
- [Causality-Driven Slow-Down and Speed-Up of Diffusion in Non-Markovian Temporal Networks](https://mlsanity.com/paper/1307.4030/) (2013)
- [When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks](https://mlsanity.com/paper/1702.05499/) (2017)
- [Mapping higher-order network flows in memory and multilayer networks with Infomap](https://mlsanity.com/paper/1706.04792/) (2017)
- [Merge-split Markov chain Monte Carlo for community detection](https://mlsanity.com/paper/2003.07070/) (2020)
- [Modeling sequences and temporal networks with dynamic community structures](https://mlsanity.com/paper/1509.04740/) (2015)
- [Flow stability for dynamic community detection](https://mlsanity.com/paper/2101.06131/) (2021)
- [An Exact No Free Lunch Theorem for Community Detection](https://mlsanity.com/paper/1903.10092/) (2019)
- [Bayesian Higher Order Hidden Markov Models](https://mlsanity.com/paper/1805.12201/) (2018)
- [Consistent Bayesian Community Detection](https://mlsanity.com/paper/2101.06531/) (2021)
- [Predicting Influential Higher-Order Patterns in Temporal Network Data](https://mlsanity.com/paper/2107.12100/) (2021)

## Abstract (arXiv)

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.