5 citations · 14 across the 5 of their papers we have counts for
5 papers
MixKG: Mixing for harder negative samples in knowledge graph
Feihu Che, Guohua Yang, Pengpeng Shao +2
Knowledge graph embedding~(KGE) aims to represent entities and relations into low-dimensional vectors for many real-world applications. The representations of entities and relation…
Knowledge graph enhanced recommender system
Zepeng Huai, Jianhua Tao, Feihu Che +2
Knowledge Graphs (KGs) have shown great success in recommendation. This is attributed to the rich attribute information contained in KG to improve item and user representations as…
Multi-Level Graph Contrastive Learning
Pengpeng Shao, Tong Liu, Dawei Zhang +3
Graph representation learning has attracted a surge of interest recently, whose target at learning discriminant embedding for each node in the graph. Most of these representation m…
Tucker decomposition-based Temporal Knowledge Graph Completion
Pengpeng Shao, Guohua Yang, Dawei Zhang +3
Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the…
Self-supervised Graph Representation Learning via Bootstrapping
Feihu Che, Guohua Yang, Dawei Zhang +3
Graph neural networks~(GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNN…