  With the development of foundation models such as large language models,
zero-shot transfer learning has become increasingly significant. This is
highlighted by the generative capabilities of NLP models like GPT-4, and the
retrieval-based approaches of CV models like CLIP, both of which effectively
bridge the gap between seen and unseen data. In the realm of graph learning,
the continuous emergence of new graphs and the challenges of human labeling
also amplify the necessity for zero-shot transfer learning, driving the
exploration of approaches that can generalize across diverse graph data without
necessitating dataset-specific and label-specific fine-tuning. In this study,
we extend such paradigms to zero-shot transferability in graphs by introducing
ZeroG, a new framework tailored to enable cross-dataset generalization.
Addressing the inherent challenges such as feature misalignment, mismatched
label spaces, and negative transfer, we leverage a language model to encode
both node attributes and class semantics, ensuring consistent feature
dimensions across datasets. We also propose a prompt-based subgraph sampling
module that enriches the semantic information and structure information of
extracted subgraphs using prompting nodes and neighborhood aggregation,
respectively. We further adopt a lightweight fine-tuning strategy that reduces
the risk of overfitting and maintains the zero-shot learning efficacy of the
language model. The results underscore the effectiveness of our model in
achieving significant cross-dataset zero-shot transferability, opening pathways
for the development of graph foundation models. Codes and data are available at
https://github.com/NineAbyss/ZeroG.
