  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.
