  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.
