LLaGA: Large Language and Graph Assistant
CoRR(2024)
摘要
Graph Neural Networks (GNNs) have empowered the advance in graph-structured
data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4
has heralded a new era in deep learning. However, their application to graph
data poses distinct challenges due to the inherent difficulty of translating
graph structures to language. To this end, we introduce the Large
Language and Graph Assistant
(LLaGA), an innovative model that effectively integrates LLM
capabilities to handle the complexities of graph-structured data. LLaGA retains
the general-purpose nature of LLMs while adapting graph data into a format
compatible with LLM input. LLaGA achieves this by reorganizing graph nodes to
structure-aware sequences and then mapping these into the token embedding space
through a versatile projector. LLaGA excels in versatility, generalizability
and interpretability, allowing it to perform consistently well across different
datasets and tasks, extend its ability to unseen datasets or tasks, and provide
explanations for graphs. Our extensive experiments across popular graph
benchmarks show that LLaGA delivers outstanding performance across four
datasets and three tasks using one single model, surpassing state-of-the-art
graph models in both supervised and zero-shot scenarios. Our code is available
at
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要