Combining Loss-aware Curriculum Learning with Incomplete Graph Neural Networks

Abstract

Graph neural networks (GNNs) have achieved great success in node classification tasks. However, most graph neural networks are incomplete. For example, the reference of each article is subjectively introduced by the author in the citation network, which leads to an incomplete citation network, especially the imbalance of categories. Directly training a GNN classifier with raw data tends to under-represent samples from minority classes, leading to suboptimal performance. This paper presents a novel framework, named CL2IGNN, in which an embedding space is constructed to encode the similarity among the nodes. New samples are synthesized in this space to ensure their genuineness. Additionally, an edge generator is trained simultaneously to model the relational information and provide it to these new samples. In order to improve the accuracy of node classification, we assess the quality of each data node and progressively incorporate the training dataset into the model, increasing the difficulty step by step. Our approach can effectively reduce bias and variance, mitigate the impact of noisy data, and improve overall accuracy. The code is public at https://anonymous.4open.science/r/CL2IGNN-1832.