Graph Diffusion Models vs. Sparse GNNs: Memory Scaling on Large Perturbed Graphs

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the memory complexity scaling of graph diffusion models during inference compare to sparse GNN architectures when processing large graphs with high-frequency spectral perturbations. Abstract Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the memory complexity scaling of graph diffusion models during inference compare to sparse GNN architectures when processing large graphs with high-frequency spectral perturbations?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance