STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs

• URL: http://arxiv.org/abs/2407.12860v1
• Date: Wed, 10 Jul 2024 08:50:25 GMT
• Title: STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs
• Authors: Aaron Zolnai-Lucas, Jack Boylan, Chris Hokamp, Parsa Ghaffari,
• Abstract summary: We present a method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs) Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training.
• Score: 1.4624458429745086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. STAGE achieves competitive results on various node classification benchmarks while also maintaining a simplicity in implementation relative to current state-of-the-art (SoTA) techniques. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training, enabling pipelines that are simpler than current SoTA approaches which require multiple expensive training and prompting stages. We also implement diffusion-pattern GNNs in an effort to make this pipeline scalable to graphs beyond academic benchmarks.

Related papers

• Language Models are Graph Learners [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Empower Text-Attributed Graphs Learning with Large Language Models (LLMs) [5.920353954082262]
  We propose a plug-and-play approach to empower text-attributed graphs through node generation using Large Language Models (LLMs) We employ an edge predictor to capture the structural information inherent in the raw dataset and integrate the newly generated samples into the original graph. Experiments demonstrate the outstanding performance of our proposed paradigm, particularly in low-shot scenarios.
  arXiv  Detail & Related papers  (2023-10-15T16:04:28Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [59.74814230246034]
  Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities. We investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors.
  arXiv  Detail & Related papers  (2023-07-07T05:31:31Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.