From topology learning to graph generation: A unifying perspective
stat.ML, cs.LG, eess.SP
Submitted: 2026-09-02
Updated: 2026-09-02
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- A Survey on Graph Structure Learning: Progress and Opportunities
- Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
- Graph Structure Learning with Interpretable Bayesian Neural Networks
- Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativity
- Learning Product Graphs Underlying Smooth Graph Signals
- GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
- The Principles of Diffusion Models
- Cometh: A continuous-time discrete-state graph diffusion model
- Principled Latent Diffusion for Graphs via Laplacian Autoencoders
- GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
- Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation
- Approximation Methods for Bilevel Programming
- Task-driven Heterophilic Graph Structure Learning
- Learning Graph Topology with Functional Priors via Bilevel Optimization
- From Uniform to Learned Graph Priors: Diffusion for Structure Discovery
- A Markov Random Field model for Hypergraph-based Machine Learning
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