Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno
are trained on a huge amount of language corpus, images, videos, and audio that
are extremely diverse from numerous domains. This training paradigm over
diverse well-curated data lies at the heart of generating creative and sensible
content. However, all previous graph generative models (e.g., GraphRNN, MDVAE,
MoFlow, GDSS, and DiGress) have been trained only on one dataset each time,
which cannot replicate the revolutionary success achieved by LGMs in other
fields. To remedy this crucial gap, we propose a new class of graph generative
model called Large Graph Generative Model (LGGM) that is trained on a large
corpus of graphs (over 5000 graphs) from 13 different domains. We empirically
demonstrate that the pre-trained LGGM has superior zero-shot generative
capability to existing graph generative models. Furthermore, our pre-trained
LGGM can be easily fine-tuned with graphs from target domains and demonstrate
even better performance than those directly trained from scratch, behaving as a
solid starting point for real-world customization. Inspired by Stable
Diffusion, we further equip LGGM with the capability to generate graphs given
text prompts (Text-to-Graph), such as the description of the network name and
domain (i.e., "The power-1138-bus graph represents a network of buses in a
power distribution system."), and network statistics (i.e., "The graph has a
low average degree, suitable for modeling social media interactions."). This
Text-to-Graph capability integrates the extensive world knowledge in the
underlying language model, offering users fine-grained control of the generated
graphs. We release the code, the model checkpoint, and the datasets at
this https URL

我们提出了一种名为大图生成模型（LGGM）的新型图生成模型，该模型在来自 13 个不同领域的大量图形语料库（超过 5000 个图形）上进行训练，并且在零样本生成能力方面优于现有的图生成模型。此外，我们的预训练 LGGM 可以轻松地通过来自目标领域的图形进行微调，并展现出比从头开始训练的模型更好的性能，作为实际定制的坚实起点。该模型还具备了根据文本提示生成图形的功能（文本到图形），该功能将底层语言模型中的广泛世界知识整合在一起，为用户提供对生成图形的细粒度控制。我们在下面的链接上发布了代码、模型检查点和数据集。