From Graphs to Feeders: Constraint-Guided Diffusion for Rule-Compliant Feeder Generation

arXiv:2609.29879 · cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Transformer — 41× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Wasserstein Distance — 4× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Department of Energy — 2× in this paper
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Electric Power Research Institute — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Relational inductive biases, deep learning, and graph networks
• Diffusion Models for Graphs Benefit From Discrete State Spaces
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis
• GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
• Sparse Training of Discrete Diffusion Models for Graph Generation
• GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks