Graph Foundation Model (GFM) is a new trending research topic in the graph
domain, aiming to develop a graph model capable of generalizing across
different graphs and tasks. However, a versatile GFM has not yet been achieved.
The key challenge in building GFM is how to enable positive transfer across
graphs with diverse structural patterns. Inspired by the existing foundation
models in the CV and NLP domains, we propose a novel perspective for the GFM
development by advocating for a ``graph vocabulary'', in which the basic
transferable units underlying graphs encode the invariance on graphs. We ground
the graph vocabulary construction from essential aspects including network
analysis, theoretical foundations, and stability. Such a vocabulary perspective
can potentially advance the future GFM design following the neural scaling
laws.

图基础模型（GFM）是图领域中一个新兴的研究课题，旨在发展一种能够概括不同图和任务的图模型。我们提出了一个新颖的视角 ——“图词汇表”，通过在图中编码基本可转移单元的不变性来建立图词汇表，从而解决在构建 GFM 中遇到的跨结构模式图之间正向传递的关键挑战，这一视角可以潜在地推进未来 GFM 设计遵循神经规模定律。