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Logic-Guided Graph Denoising Diffusion Models

Paper recorded by Signals 4 on 2026-10-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.LG · 机器学习 · first seen 2026-10-19

Abstract

Graph diffusion models are competitive approaches for generating complex graph-structured data. However, when such distributions have strict structural constraints, diffusion models may fail to capture them exactly, resulting in the generation of invalid graphs. In this paper, we propose a method for grounding graph diffusion models in domain-specific knowledge via First Order Logic (FOL), leverag

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#260 most recent of 749 cs.LG papers we have recorded · ↑ newer: Mesoscopic learning dynamics of extensive-width neural networks · ↓ older: Universal Semantic Enhancement for Weakly Supervised Anomaly Detection
Cite this page: Logic-Guided Graph Denoising Diffusion Models: the #260 most recent of 749 cs.LG papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/logic-guided-graph-denoising-diffusion-models.html
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