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 \url{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。

LLaGA：大型语言和图形助手