When Graph Structure Hurts
Lightweight path ranking for dense KG-RAG · ICMLC 2026
Overview
Research accepted at ICMLC 2026 (presentation, Feb 2026). We study retrieval over dense academic citation graphs and ask: when does graph structure actually help KG-RAG?
Key result
A lightweight MLP-based path scorer achieves 93.9% AUC, outperforming GCN and GAT models while using 13× fewer parameters. In dense graphs, heavy message-passing architectures can amplify noise rather than signal.
Method
- KG-augmented RAG pipeline over citation graphs
- Candidate path generation, then learned ranking of paths feeding the retriever
- Systematic comparison: MLP scorer vs. GCN vs. GAT under matched budgets
Why it matters
Most KG-RAG work assumes more graph machinery = better retrieval. This shows the opposite can hold in dense regimes, useful for anyone deploying KG-RAG where inference cost matters.