Graph Contrastive Anomaly Detection and Supervised GNN Inference Latency on ogbn-arxiv

Abstract

Abstract: This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does the inference latency of graph contrastive anomaly detection models compare to supervised GNN baselines when evaluated on the ogbn-arxiv benchmark using throughput (queries per second) as. Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number of sampled graph nodes and aggregated GNN features. This makes it challenging to exploit GPUs. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference latency of graph contrastive anomaly detection models compare to supervised GNN baselines when evaluated on the ogbn-arxiv benchmark using throughput (queries per second) as the metric?

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