Perturbation Scaling Effects on XSimGCL and GCL Inference Efficiency with NDCG Constraints

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the scaling of perturbation levels affect the inference efficiency of XSimGCL and other GCL methods on the Reddit dataset while maintaining a minimum NDCG@10 score of 0.7. Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic information, or (2) using domain knowledge to maintain salient features, which undermines the. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the scaling of perturbation levels affect the inference efficiency of XSimGCL and other GCL methods on the Reddit dataset while maintaining a minimum NDCG@10 score of 0.7?

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