  Large Language Models (LLMs) have revolutionized the fields of computer
vision (CV) and natural language processing (NLP). One of the most notable
advancements of LLMs is that a single model is trained on vast and diverse
datasets spanning multiple domains -- a paradigm we term `All in One'. This
methodology empowers LLMs with super generalization capabilities, facilitating
an encompassing comprehension of varied data distributions. Leveraging these
capabilities, a single LLM demonstrates remarkable versatility across a variety
of domains -- a paradigm we term `One for All'. However, applying this idea to
the graph field remains a formidable challenge, with cross-domain pretraining
often resulting in negative transfer. This issue is particularly important in
few-shot learning scenarios, where the paucity of training data necessitates
the incorporation of external knowledge sources. In response to this challenge,
we propose a novel approach called Graph COordinators for PrEtraining (GCOPE),
that harnesses the underlying commonalities across diverse graph datasets to
enhance few-shot learning. Our novel methodology involves a unification
framework that amalgamates disparate graph datasets during the pretraining
phase to distill and transfer meaningful knowledge to target tasks. Extensive
experiments across multiple graph datasets demonstrate the superior efficacy of
our approach. By successfully leveraging the synergistic potential of multiple
graph datasets for pretraining, our work stands as a pioneering contribution to
the realm of graph foundational model.
