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[2601.02241] From Mice to Trains: Amortized Bayesian Inference on Graph Data
Summary
This article presents a novel approach to Amortized Bayesian Inference (ABI) tailored for graph data, addressing challenges in posterior estimation across diverse domains such as biology and logistics.
Why It Matters
As graphs are increasingly used in various fields, developing efficient methods for inference on graph-structured data is crucial. This research enhances ABI by integrating neural networks, offering a scalable solution for complex data analysis, which can significantly impact fields reliant on graph data.
Key Takeaways
- ABI is adapted for graph data to improve posterior estimation.
- The method employs permutation-invariant graph encoders for effective data representation.
- Evaluation includes performance assessments in synthetic settings and real-world applications.
- The approach addresses challenges of scalability and long-range dependencies in graph data.
- Multiple neural architectures are explored for their effectiveness in the proposed framework.
Statistics > Machine Learning arXiv:2601.02241 (stat) [Submitted on 5 Jan 2026 (v1), last revised 16 Feb 2026 (this version, v3)] Title:From Mice to Trains: Amortized Bayesian Inference on Graph Data Authors:Svenja Jedhoff, Elizaveta Semenova, Aura Raulo, Anne Meyer, Paul-Christian Bürkner View a PDF of the paper titled From Mice to Trains: Amortized Bayesian Inference on Graph Data, by Svenja Jedhoff and 4 other authors View PDF HTML (experimental) Abstract:Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data requires methods that are permutation-invariant, scalable across varying sizes and sparsities, and capable of capturing complex long-range dependencies, making posterior estimation on graph parameters particularly challenging. Amortized Bayesian Inference (ABI) is a simulation-based framework that employs generative neural networks to enable fast, likelihood-free posterior inference. We adapt ABI to graph data to address these challenges to perform inference on node-, edge-, and graph-level parameters. Our approach couples permutation-invariant graph encoders with flexible neural posterior estimators in a two-module pipeline: a summary network maps attributed graphs to fixed-length representations, and an inference network approximates the posterior over parameters. In this setting, several neural architectures can serve as the summary network...
