Language is All a Graph Needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, Yongfeng Zhang

TL;DR
This paper introduces InstructGLM, a novel approach that uses instruction-finetuned large language models to perform graph learning tasks, surpassing traditional GNNs on multiple datasets by leveraging natural language descriptions of graph structures.
Contribution
It presents a new generative graph learning framework using LLMs with natural language instructions, replacing GNNs as a foundation model for graph analysis.
Findings
Outperforms GNN baselines on ogbn-arxiv, Cora, and PubMed datasets.
Uses natural language to describe multi-scale graph structures.
Demonstrates effectiveness of generative LLMs for graph tasks.
Abstract
The emergence of large-scale pre-trained language models has revolutionized various AI research domains. Transformers-based Large Language Models (LLMs) have gradually replaced CNNs and RNNs to unify fields of computer vision and natural language processing. Compared with independent data samples such as images, videos or texts, graphs usually contain rich structural and relational information. Meanwhile, language, especially natural language, being one of the most expressive mediums, excels in describing complex structures. However, existing work on incorporating graph problems into the generative language modeling framework remains very limited. Considering the rising prominence of LLMs, it becomes essential to explore whether LLMs can also replace GNNs as the foundation model for graphs. In this paper, we propose InstructGLM (Instruction-finetuned Graph Language Model) with highly…
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Taxonomy
TopicsTopic Modeling · Advanced Graph Neural Networks · Natural Language Processing Techniques
