ArticleApplied network science2023
Predicting variable-length paths in networked systems using multi-order generative models.
Article in Applied network science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Concise network models of memory dynamics reveal explainable patterns in path data.Science advances · 2025Article
- Article
- Adapting to disruptions: Managing supply chain resilience through product rerouting.Science advances · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Apart from nodes and links, for many networked systems, we have access to data on paths, i.e., collections of temporally ordered variable-length node sequences that are constrained by the system's topology. Understanding the patterns in such data is key to advancing our understanding of the structure and dynamics of complex systems. Moreover, the ability to accurately model and predict paths is important for engineered systems, e.g., to optimise supply chains or provide smart mobility services. Here, we introduce MOGen, a generative modelling framework that enables both next-element and out-of-sample prediction in paths with high accuracy and consistency. It features a model selection approach that automatically determines the optimal model directly from data, effectively making MOGen parameter-free. Using empirical data, we show that our method outperforms state-of-the-art sequence modelling techniques. We further introduce a mathematical formalism that links higher-order models of paths to transition matrices of random walks in multi-layer networks.
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