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 \textbf{L}arge
\textbf{L}anguage \textbf{a}nd \textbf{G}raph \textbf{A}ssistant
(\textbf{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 https://github.com/ChenRunjin/LLaGA

LLaGA 是一个创新的模型，它将大型语言模型的能力与处理图结构数据的复杂性相结合，通过重新组织图节点并将其映射到标记嵌入空间来实现。LLaGA 在不同数据集和任务上表现出色，能够一次性使用一个模型，在受监督和零样本情况下超越最先进的图模型。