In recent years, heterogeneous graph neural networks (HGNNs) have achieved
excellent performance in handling heterogeneous information networks (HINs).
Curriculum learning is a machine learning strategy where training examples are
presented to a model in a structured order, starting with easy examples and
gradually increasing difficulty, aiming to improve learning efficiency and
generalization. To better exploit the rich information in HINs, previous
methods have started to explore the use of curriculum learning strategy to
train HGNNs. Specifically, these works utilize the absolute value of the loss
at each training epoch to evaluate the learning difficulty of each training
sample. However, the relative loss, rather than the absolute value of loss,
reveals the learning difficulty. Therefore, we propose a novel
loss-decrease-aware training schedule (LDTS). LDTS uses the trend of loss
decrease between each training epoch to better evaluating the difficulty of
training samples, thereby enhancing the curriculum learning of HGNNs for
downstream tasks. Additionally, we propose a sampling strategy to alleviate
training imbalance issues. Our method further demonstrate the efficacy of
curriculum learning in enhancing HGNNs capabilities. We call our method
Loss-decrease-aware Heterogeneous Graph Neural Networks (LDHGNN). The code is
public at this https URL

在处理异构信息网络方面，异构图神经网络（HGNN）通过课程学习策略和基于相对损失的训练进程趋势来提高学习效率与泛化能力，并通过采样策略解决训练不平衡问题。