Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)

• URL: http://arxiv.org/abs/2310.09872v1
• Date: Sun, 15 Oct 2023 16:04:28 GMT
• Title: Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)
• Authors: Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, Xuecang Zhang
• Abstract summary: 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.
• Score: 5.920353954082262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for acquiring text representations as node features, which are subsequently fed into Graph Neural Networks (GNNs) for training. Recently, the advent of Large Language Models (LLMs) has introduced their powerful capabilities in information retrieval and text generation, which can greatly enhance the text attributes of graph data. Furthermore, the acquisition and labeling of extensive datasets are both costly and time-consuming endeavors. Consequently, few-shot learning has emerged as a crucial problem in the context of graph learning tasks. In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs. Specifically, we utilize LLMs to extract semantic information from the labels and generate samples that belong to these categories as exemplars. Subsequently, 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. This approach harnesses LLMs for enhancing class-level information and seamlessly introduces labeled nodes and edges without modifying the raw dataset, thereby facilitating the node classification task in few-shot scenarios. Extensive experiments demonstrate the outstanding performance of our proposed paradigm, particularly in low-shot scenarios. For instance, in the 1-shot setting of the ogbn-arxiv dataset, ENG achieves a 76% improvement over the baseline model.

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