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

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

Autonomous literature synthesis. Automated review score: 7.5/10. Full text and citation available at Assignee Research.