Graph Diffusion Models vs. Sparse GNNs: Memory Complexity in Large-Scale Spectral Perturbations

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the inference memory complexity of graph diffusion models compare to sparse GNNs when processing large graphs with high-frequency spectral perturbations. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference memory complexity of graph diffusion models compare to sparse GNNs when processing large graphs with high-frequency spectral perturbations?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.