Mul-GAD Inference Throughput vs. GraphSAGE and GAT on Large-Scale Graphs

Abstract

Abstract: This report synthesises findings from 3 peer-reviewed papers addressing the following research question: How does the inference throughput of Mul-GAD compare to GraphSAGE and GAT on large-scale graph datasets when measured in graphs per second. MOTIVATION: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering. 10 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference throughput of Mul-GAD compare to GraphSAGE and GAT on large-scale graph datasets when measured in graphs per second?

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