# Efficient Code Analysis via Graph Representation Learning-Guided Large Language Models

arXiv: https://arxiv.org/abs/2601.12890 · HTML: https://mlsanity.com/paper/2601.12890/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection](https://mlsanity.com/paper/2607.03350/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have significantly advanced code analysis tasks, yet they struggle to detect malicious behaviors fragmented across files, whose intricate dependencies easily get lost in the vast amount of benign code. We therefore propose a graph-centric attention acquisition pipeline that enhances LLMs' ability to localize malicious behavior. The approach parses a project into a code graph, uses an LLM to encode nodes with semantic and structural signals, and trains a Graph Neural Network (GNN) under sparse supervision. The GNN performs an initial detection, and by interpreting these predictions, identifies key code sections that are most likely to contain malicious behavior. These influential regions are then used to guide the LLM's attention for in-depth analysis. This strategy significantly reduces interference from irrelevant context while maintaining low annotation costs. Extensive experiments show that the method consistently outperforms existing approaches on multiple public and custom datasets, highlighting its potential for practical deployment in software security scenarios.