  Graph neural networks (GNNs) have shown promise in addressing graph-related
problems, including node classification. However, conventional GNNs assume an
even distribution of data across classes, which is often not the case in
real-world scenarios, where certain classes are severely underrepresented. This
leads to suboptimal performance of standard GNNs on imbalanced graphs. In this
paper, we introduce a unique approach that tackles imbalanced classification on
graphs by considering graph heterophily. We investigate the intricate
relationship between class imbalance and graph heterophily, revealing that
minority classes not only exhibit a scarcity of samples but also manifest lower
levels of homophily, facilitating the propagation of erroneous information
among neighboring nodes. Drawing upon this insight, we propose an efficient
method, called Fast Im-GBK, which integrates an imbalance classification
strategy with heterophily-aware GNNs to effectively address the class imbalance
problem while significantly reducing training time. Our experiments on
real-world graphs demonstrate our model's superiority in classification
performance and efficiency for node classification tasks compared to existing
baselines.
