no code implementations • 26 Apr 2022 • Pengpeng Shao, Tong Liu, Feihu Che, Dawei Zhang, JianHua Tao
Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks.
no code implementations • 19 Feb 2022 • Feihu Che, Guohua Yang, Pengpeng Shao, Dawei Zhang, JianHua Tao
The representations of entities and relations are learned via contrasting the positive and negative triplets.
no code implementations • 6 Jul 2021 • Pengpeng Shao, Tong Liu, Dawei Zhang, JianHua Tao, Feihu Che, Guohua Yang
In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs.
1 code implementation • 16 Nov 2020 • Pengpeng Shao, Guohua Yang, Dawei Zhang, JianHua Tao, Feihu Che, Tong Liu
Developing the model for temporal knowledge graphs completion is an increasingly important task.
no code implementations • 10 Nov 2020 • Feihu Che, Guohua Yang, Dawei Zhang, JianHua Tao, Pengpeng Shao, Tong Liu
In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB.