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arXiv Machine Learning

Time-Varying Graph Learning with Constraints on Graph Temporal Variation

The paper introduces a new framework for learning time‑varying graphs from spatiotemporal data, leveraging a prior on signal temporal behavior to estimate graphs from few measurements. It adds three convex regularization terms that enforce sparsity in the temporal changes of the network, and presents a scalable algorithm to solve the resulting optimization problem. Experiments on synthetic data and real datasets—including point clouds, temperature readings, and EEG signals—show that the method outperforms existing state‑of‑the‑art approaches.

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arXiv Machine Learning
Sep 24

Graph Learning with Spectral Connectivity Priors for Scarce Data

The paper introduces Spectral Connectivity-Regularized Graph Learning (SCoGL), a method for learning sparse graphs from limited data by incorporating Laplacian spectral priors that promote global connectivity. SCoGL extends the graphical lasso objective with a connectivity prior derived from Laplacian eigenvalues and uses projected gradient descent with Armijo backtracking for optimization. Experiments demonstrate that SCoGL improves graph recovery and enhances downstream tasks such as graph signal denoising when observations are scarce.

By Mingxiao Liu (Tsinghua University, China), Bahar Oveisgharan (York University, Canada), Bingyan Zou (Tsinghua University, China), Gene Cheung (York University, Canada), H. Vicky Zhao (Tsinghua University, China), Feifei Gao (Tsinghua University, China)
efficiency
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arXiv Machine Learning
Sep 17

Stable Filters for Generative Modeling of Graph Signals

The paper studies the stability of graph-aware continuous‑time generative models that use a graph filter combined with a learned graph neural network. It derives explicit Wasserstein bounds showing how relative graph perturbations affect the generated distributions, and proposes a principled framework for designing stable graph filters that preserve heat‑diffusion smoothing while improving structural stability. Experiments on synthetic and fMRI data demonstrate that these stable filters enhance robustness and match or surpass the generative quality of a heat‑equation baseline.

By Martin Schmidt, Gonzalo Mateos
diffusion
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arXiv Machine Learning
Jul 17

What Do Temporal Graph Learning Models Learn?

arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.

By Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier
benchmarkssafety
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arXiv Machine Learning
Sep 23

Spatiotemporal Kronecker Covariance Neural Networks

The paper introduces the Kronecker coVariance Neural Network (KVNN), a temporal graph neural network that models spatiotemporal covariance matrices as sums of Kronecker products, decoupling spatial and temporal dependencies. KVNNs perform filtering on spatial and temporal components, enabling expressive processing, rigorous spectral analysis, and provable stability to finite-sample estimation errors. Experiments on five real-world datasets show that KVNNs deliver strong forecasting performance with fewer trainable parameters than competing methods and maintain consistency under estimation noise.

By Andrea Cavallo, Athanasios Georgoutsos, Elvin Isufi
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