mlsanityData to 14 Oct 2026

LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search

arXiv 2502.10459 ↗First posted 12 Feb 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

Graph Neural Architecture Search (GNAS) facilitates the automatic design of Graph Neural Networks (GNNs) tailored to specific downstream graph learning tasks. However, existing GNAS approaches often require manual adaptation to new graph search spaces, necessitating substantial code optimization and domain-specific knowledge. To address this challenge, we present LLM4GNAS, a toolkit for GNAS that leverages the generative capabilities of Large Language Models (LLMs). LLM4GNAS includes an algorithm library for graph neural architecture search algorithms based on LLMs, enabling the adaptation of GNAS methods to new search spaces through the modification of LLM prompts. This approach reduces the need for manual intervention in algorithm adaptation and code modification. The LLM4GNAS toolkit is extensible and robust, incorporating LLM-enhanced graph feature engineering, LLM-enhanced graph neural architecture search, and LLM-enhanced hyperparameter optimization. Experimental results indicate that LLM4GNAS outperforms existing GNAS methods on tasks involving both homogeneous and heterogeneous graphs.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

30 of 49 references.

References · 30

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18 more
  1. 2021How Attentive are Graph Attention Networks?cited by 835
  2. 2019Heterogeneous Graph Attention Networkcited by 734
  3. 2020Heterogeneous Graph Transformercited by 582
  4. 2021Beyond Low-frequency Information in Graph Convolutional Networkscited by 362
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  16. 2021AutoGEL: An Automated Graph Neural Network with Explicit Link Informationcited by 17
  17. 2023Adversarially Robust Neural Architecture Search for Graph Neural Networkscited by 7
  18. 2023Heterogeneous Graph Neural Architecture Search with GPT-4cited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learningcited by 3
  2. 2025ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architecturescited by 1
  3. 2025X-Node: Self-Explanation is All We Needcited by 0
  4. 2026Unifying Graph Neural Networks Through a Common Layer Equationcited by 0