no code implementations • 20 Nov 2023 • YiFan Li, Zhen Tan, Kai Shu, Zongsheng Cao, Yu Kong, Huan Liu
Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when the data is scarce or imbalanced.
1 code implementation • 24 Jun 2022 • Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks.
1 code implementation • 24 Jun 2022 • Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Qingming Huang
To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information.