Given the ubiquity of graph data and its applications in diverse domains,
building a Graph Foundation Model (GFM) that can work well across different
graphs and tasks with a unified backbone has recently garnered significant
interests. A major obstacle to achieving this goal stems from the fact that
graphs from different domains often exhibit diverse node features. Inspired by
multi-modal models that align different modalities with natural language, the
text has recently been adopted to provide a unified feature space for diverse
graphs. Despite the great potential of these text-space GFMs, current research
in this field is hampered by two problems. First, the absence of a
comprehensive benchmark with unified problem settings hinders a clear
understanding of the comparative effectiveness and practical value of different
text-space GFMs. Second, there is a lack of sufficient datasets to thoroughly
explore the methods' full potential and verify their effectiveness across
diverse settings. To address these issues, we conduct a comprehensive benchmark
providing novel text-space datasets and comprehensive evaluation under unified
problem settings. Empirical results provide new insights and inspire future
research directions. Our code and data are publicly available from
https://github.com/CurryTang/TSGFM.

建立一个可以在不同图和任务中很好工作的图基础模型（GFM），通过统一的骨干结构近期引起了广泛关注。尽管文本空间的 GFM 存在巨大潜力，但目前的研究面临两个问题：缺乏统一问题设置的综合基准，以及缺乏足够的数据集来全面探索方法的潜力。为了解决这些问题，我们进行了综合基准的研究，并提供了新的文本空间数据集和全面的评估。实证结果提供了新的见解，并启发了未来的研究方向。

文本空间图基础模型：全面的基准测试和新洞察

Text-space Graph Foundation Models: Comprehensive Benchmarks and New  Insights

Graph learning has become indispensable for interpreting and harnessing
relational data in diverse fields, ranging from recommendation systems to
social network analysis. In this context, a variety of GNNs have emerged as
promising methodologies for encoding the structural information of graphs. By
effectively capturing the graph's underlying structure, these GNNs have shown
great potential in enhancing performance in graph learning tasks, such as link
prediction and node classification. However, despite their successes, a
significant challenge persists: these advanced methods often face difficulties
in generalizing to unseen graph data that significantly differs from the
training instances. In this work, our aim is to advance the graph learning
paradigm by developing a general graph foundation model. This model is designed
to understand the complex topological patterns present in diverse graph data,
enabling it to excel in zero-shot graph learning tasks across different
downstream datasets. To achieve this goal, we address several key technical
challenges in our OpenGraph model. Firstly, we propose a unified graph
tokenizer to adapt our graph model to generalize well on unseen graph data,
even when the underlying graph properties differ significantly from those
encountered during training. Secondly, we develop a scalable graph transformer
as the foundational encoder, which effectively captures node-wise dependencies
within the global topological context. Thirdly, we introduce a data
augmentation mechanism enhanced by a LLM to alleviate the limitations of data
scarcity in real-world scenarios. Extensive experiments validate the
effectiveness of our framework. By adapting our OpenGraph to new graph
characteristics and comprehending the nuances of diverse graphs, our approach
achieves remarkable zero-shot graph learning performance across various
settings and domains.

我们开发了一个通用的图基础模型，通过理解不同基于图的数据中的复杂拓扑模式，使其在不同下游数据集上出色地完成零样本图学习任务。

OpenGraph：走向开放图形基础模型

OpenGraph: Towards Open Graph Foundation Models

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。