Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
Zhen Hao Wong Hansi Yang Xiaoyi Fu Quanming Yao
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning
models designed specifically for heterogeneous graphs, which are graphs that
contain different types of nodes and edges. This paper investigates the
application of curriculum learning techniques to improve the performance and
robustness of Heterogeneous Graph Neural Networks (GNNs). To better classify
the quality of the data, we design a loss-aware training schedule, named LTS
that measures the quality of every nodes of the data and incorporate the
training dataset into the model in a progressive manner that increases
difficulty step by step. LTS can be seamlessly integrated into various
frameworks, effectively reducing bias and variance, mitigating the impact of
noisy data, and enhancing overall accuracy. Our findings demonstrate the
efficacy of curriculum learning in enhancing HGNNs capabilities for analyzing
complex graph-structured data. The code is public at https:
//github.com/LARS-research/CLGNN/.