  Foundation models have emerged as critical components in a variety of
artificial intelligence applications, and showcase significant success in
natural language processing and several other domains. Meanwhile, the field of
graph machine learning is witnessing a paradigm transition from shallow methods
to more sophisticated deep learning approaches. The capabilities of foundation
models to generalize and adapt motivate graph machine learning researchers to
discuss the potential of developing a new graph learning paradigm. This
paradigm envisions models that are pre-trained on extensive graph data and can
be adapted for various graph tasks. Despite this burgeoning interest, there is
a noticeable lack of clear definitions and systematic analyses pertaining to
this new domain. To this end, this article introduces the concept of Graph
Foundation Models (GFMs), and offers an exhaustive explanation of their key
characteristics and underlying technologies. We proceed to classify the
existing work related to GFMs into three distinct categories, based on their
dependence on graph neural networks and large language models. In addition to
providing a thorough review of the current state of GFMs, this article also
outlooks potential avenues for future research in this rapidly evolving domain.
