Out-of-Distribution Graph Models Merging

• URL: http://arxiv.org/abs/2506.03674v1
• Date: Wed, 04 Jun 2025 08:04:07 GMT
• Title: Out-of-Distribution Graph Models Merging
• Authors: Yidi Wang, Jiawei Gu, pei Xiaobing, Xubin Zheng, Xiao Luo, Pengyang Wang, Ziyue Qiao,
• Abstract summary: We propose a graph generation strategy that instantiates the mixture distribution of multiple domains.<n>Our framework is architecture-agnostic and can operate without any source/target domain data.
• Score: 9.926662903459691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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