LightGCL vs. SGL and GCA: Inference Efficiency at Scale in Graph-Based Recommendation

Abstract

Abstract: This report synthesises findings from 2 peer-reviewed papers addressing the following research question: How does the inference efficiency of LightGCL compare to SGL and GCA when scaling to large-scale recommendation datasets like Amazon-1M and MovieLens-20M in terms of runtime and memory usage. In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference efficiency of LightGCL compare to SGL and GCA when scaling to large-scale recommendation datasets like Amazon-1M and MovieLens-20M in terms of runtime and memory usage?

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