Out-of-Distribution Graph Models Merging
Yidi Wang, Ziyue Qiao, Jiawei Gu, Xubin Zheng, Pengyang Wang, pei Xiaobing, Xiao Luo
Abstract
This paper studies a novel problem of out-of-distribution graph models merging, which aims to construct a generalized model from multiple graph models pre-trained on different domains with distribution discrepancy. This problem is challenging because of the difficulty in learning domain-invariant knowledge implicitly in model parameters and consolidating expertise from potentially heterogeneous GNN backbones. In this work, we propose a graph generation strategy that instantiates the mixture distribution of multiple domains. Then, we merge and fine-tune the pre-trained graph models via a MoE module and a masking mechanism for generalized adaptation. Our framework is architecture-agnostic and can operate without any source/target domain data. Both theoretical analysis and experimental results demonstrate the effectiveness of our approach in addressing the model generalization problem.
BibTeX
@inproceedings{
wang2026outofdistribution,
title={Out-of-Distribution Graph Models Merging},
author={Yidi Wang and Ziyue Qiao and Jiawei Gu and Xubin Zheng and Pengyang Wang and pei Xiaobing and Xiao Luo},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=93Y7jSUSpk}
}