KLCE: Regularized Imbalance Node-classification Via KL-divergence and Cross-Entropy
This paper introduces a novel regularization based on KL-divergence and cross entropy for imbalance node classification via Graph neural networks. We evaluate the performance of our approach on several benchmark datasets and compare it with state-of-the-art methods. The experimental results demonstrate the effectiveness of our proposed method in addressing imbalance node classification tasks.
