25 citations · 51 across the 5 of their papers we have counts for
5 papers
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li, Xu Chen, Chaozhuo Li +8
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize int…
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju +2
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most exist…
Test-Time Training for Graph Neural Networks
Yiqi Wang, Chaozhuo Li, Wei Jin +4
Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task. However, a performance gap between the training set and the test set has often been not…
Learning on Large-scale Text-attributed Graphs via Variational Inference
Jianan Zhao, Meng Qu, Chaozhuo Li +5
This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both…
HousE: Knowledge Graph Embedding with Householder Parameterization
Rui Li, Jianan Zhao, Chaozhuo Li +9
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can on…