论文标题
图形神经网络分类任务中的不平衡节点处理方法
Imbalanced Node Processing Method in Graph Neural Network Classification Task
论文作者
论文摘要
近年来，图神经网络（GNN）中的节点分类任务已迅速发展，推动了各个领域的研究发展。但是，图形数据中存在大量的类失衡，并且不同类别的数量之间存在很大的差距，从而在分类中产生了次优的结果。提出解决不平衡问题的解决方案对于我们的下游任务的成功发展变得必不可少。因此，我们从损失函数开始，然后尝试找到一个可以有效地解决图形节点不平衡以参与节点分类任务的失衡的损失函数。因此，我们将GHMC丢失引入图形神经网络中，以处理并非边缘的困难样本。减轻边际样品和简单样品的损失贡献。多个基准测试的实验表明，我们的方法可以有效地解决阶级不平衡问题，而我们的方法与传统损失函数相比提高了3％的准确性。
In recent years, the node classification task in graph neural networks(GNNs) has developed rapidly, driving the development of research in various fields. However, there are a large number of class imbalances in the graph data, and there is a large gap between the number of different classes, resulting in suboptimal results in classification. Proposing a solution to the imbalance problem has become indispensable for the successful advancement of our downstream missions. Therefore, we start with the loss function and try to find a loss function that can effectively solve the imbalance of graph nodes to participate in the node classification task. thence, we introduce GHMC Loss into the graph neural networks to deal with difficult samples that are not marginal. Attenuate the loss contribution of marginal samples and simple samples. Experiments on multiple benchmarks show that our method can effectively deal with the class imbalance problem, and our method improves the accuracy by 3% compared to the traditional loss function.
