ICML 2025

Zero-Shot Generalization of GNNs over Distinct Attribute Domains
Yangyi Shen 1 Jincheng Zhou 2 Beatrice Bevilacqua 2 Joshua Robinson 1 Charilaos Kanatsoulis 1
Jure Leskovec 1 Bruno Ribeiro 2
Abstract
Traditional Graph Neural Networks (GNNs) can-
not generalize to new graphs with node attributes
different from the training ones, making zero-shot
generalization across different node attribute do-
mains an open challenge in graph machine learn-
ing. In this paper, we propose STAGE, which en-
codes statistical dependencies between attributes
rather than individual attribute values, which may
differ in test graphs. By assuming these depen-
dencies remain invariant under changes in node
attributes, STAGE achieves provable generaliza-
tion guarantees for a family of domain shifts. Em-
pirically, STAGE demonstrates strong zero-shot
performance on medium-sized datasets: when
trained on multiple graph datasets with differ-
ent attribute spaces (varying in types and num-
ber) and evaluated on graphs with entirely new
attributes, STAGE achieves a relative improve-
ment in Hits@1 between 40% to 103% in link
prediction and a 10% improvement in node clas-
sification compared to state-of-the-art baselines.
1. Introduction
Zero-shot generalization refers to the ability of the model to
handle unseen test data without additional training or adap-
tation (Larochelle et al., 2008; Xian et al., 2017; Wang et al.,
2022). An essential prerequisite for zero-shot generalization
is a unified input space where models can learn and transfer
prediction patterns across domains. While this challenge
has been addressed in areas like natural language through
tokenization techniques that represent any text through a
fixed vocabulary (Samuel & Øvrelid, 2023), graphs present
unique challenges in achieving such unified input space.
Attributes in graphs can vary significantly across domains.
1Department of Computer Science, Stanford University, Stan-
ford, USA 2Department of Computer Science, Purdue Univ...