Fuzzy network jump models for soft dynamic clustering of graph-structured data
Federico P. Cortese
stat.ME, stat.AP, stat.ML
Submitted: 2026-08-06
Code: https://github.com/FedericoCortese/FNJM
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: We introduce a fuzzy network jump model for clustering time-varying observations indexed by the nodes of a weighted graph.
Terminology
Abstract
We introduce a fuzzy network jump model for clustering time-varying observations indexed by the nodes of a weighted graph. The framework allows flexible graph representations with spatial and temporal regularization promoting smooth soft cluster assignments across connected nodes and consecutive time points. Estimation is performed through an efficient alternating optimization scheme that exploits the quadratic structure of the regularization terms. A simulation study covering different levels of spatial dependence and cluster overlap shows that the proposed method accurately recovers the true membership probabilities and outperforms competing clustering methods. An application to traffic-network data for the city of San Francisco identifies interpretable traffic regimes and reveals their evolution over time and across connected road segments.
Sources
- Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering
- Fuzzy Jump Models for Soft and Hard Clustering of Multivariate Time Series Data
- Variational Graph Auto-Encoders
Related papers
- Doubly robust inference via calibration
- Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
- Weak-Form Recovery of Stochastic Generators
- Optimal Watermark Localization in Mixed-Source Large Language Model Texts
- Optimized Conformal Selection: Powerful Selective Inference After Data-Dependent Score Construction
- Small area learning