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 • 17 Dec 2021 • Zepeng Huai, JianHua Tao, Feihu Che, Guohua Yang, Dawei Zhang
This is attributed to the rich attribute information contained in KG to improve item and user representations as side information.
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.