Heterogeneous graph neural networks via oversampling and attribute completion.

Heterogeneous graph neural networks (HGNNs) have emerged as a powerful approach for modeling heterogeneous graphs. However, graphs often suffer from missing attributes, which poses significant challenges for HGNNs. Existing methods focus on attribute completion while neglecting the impact of class imbalance, limiting their effectiveness. To address attribute-missing heterogeneous graphs with class imbalance, we propose a novel attribute completion and class-balanced framework for HGNNs. Specifically, to mitigate class imbalance, we design a high-order augmented class-balancing module to reduce representation bias. In addition, to complete missing attributes, we propose a self-supervised heterogeneous attribute completion mechanism. To our knowledge, this is the first work focusing on class imbalance in heterogeneous graphs with missing attributes. On three benchmark datasets, our framework surpasses the state of the art attribute-missing and class-imbalance models and achieves average improvements of 2.45% in node classification and 5.17% in node clustering, respectively. Furthermore, our high-order augmented class-balancing module can be employed as a plug-in component. Its integration with baselines improves the F1 score by 0.44% on average. The code and data of this work are available at https://github.com/cherryyyy5/GraphHOAC.git.