Causality and Independence Enhancement for Biased Node Classification

• Main Authors: Chen, Guoxin, Wang, Yongqing, Guo, Fangda, Guo, Qinglang, Shao, Jiangli, Shen, Huawei, Cheng, Xueqi
• Format: Journal Article
• Language: English
• Published: 05-11-2023
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2310.09586

Most existing methods that address out-of-distribution (OOD) generalization
for node classification on graphs primarily focus on a specific type of data
biases, such as label selection bias or structural bias. However, anticipating
the type of bias in advance is extremely challenging, and designing models
solely for one specific type may not necessarily improve overall generalization
performance. Moreover, limited research has focused on the impact of mixed
biases, which are more prevalent and demanding in real-world scenarios. To
address these limitations, we propose a novel Causality and Independence
Enhancement (CIE) framework, applicable to various graph neural networks
(GNNs). Our approach estimates causal and spurious features at the node
representation level and mitigates the influence of spurious correlations
through the backdoor adjustment. Meanwhile, independence constraint is
introduced to improve the discriminability and stability of causal and spurious
features in complex biased environments. Essentially, CIE eliminates different
types of data biases from a unified perspective, without the need to design
separate methods for each bias as before. To evaluate the performance under
specific types of data biases, mixed biases, and low-resource scenarios, we
conducted comprehensive experiments on five publicly available datasets.
Experimental results demonstrate that our approach CIE not only significantly
enhances the performance of GNNs but outperforms state-of-the-art debiased node
classification methods.