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[2503.05560] Global graph features unveiled by unsupervised geometric deep learning
Summary
The paper introduces GAUDI, an unsupervised geometric deep learning framework that captures global graph features, enhancing analysis and classification of complex systems.
Why It Matters
Understanding complex systems through graph analysis is crucial in various scientific fields. GAUDI's innovative approach offers improved insights into structural variability, making it a significant advancement in machine learning applications across disciplines like biology and physics.
Key Takeaways
- GAUDI employs an hourglass architecture for effective graph analysis.
- The framework disentangles invariant features from stochastic noise.
- GAUDI shows superior performance in diverse applications, including biological systems and network modeling.
Computer Science > Machine Learning arXiv:2503.05560 (cs) [Submitted on 7 Mar 2025 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Global graph features unveiled by unsupervised geometric deep learning Authors:Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, Giovanni Volpe View a PDF of the paper titled Global graph features unveiled by unsupervised geometric deep learning, by Mirja Granfors and 5 other authors View PDF HTML (experimental) Abstract:Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce GAUDI (Graph Autoencoder Uncovering Descriptive Information), a novel unsupervised geometric deep learning framework designed to capture both local details and global structure. GAUDI employs an innovative hourglass architecture with hierarchical pooling and upsampling layers linked through skip connections, which preserve essential connectivity information throughout the encoding-decoding process. Even though identical or highly similar underlying parameters describing a system's state can lead to significant variability in graph realizations, GAUDI consistently maps them into nearby regions of a structured and continuous latent space, effectively disentangling invariant process-level features from stochastic noise. We demonstrate GAUDI's versatility across multiple applications, inc...
