58 citations · 218 across the 13 of their papers we have counts for
15 papers
Dual Space Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Shirui Pan +3
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive l…
Graph Masked Autoencoders with Transformers
Sixiao Zhang, Hongxu Chen, Haoran Yang +3
Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenari…
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Sixiao Zhang, Hongxu Chen, Xiangguo Sun +2
Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, ev…
Towards Unsupervised Deep Graph Structure Learning
Yixin Liu, Yu Zheng, Daokun Zhang +3
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated whe…
GenLabel: Mixup Relabeling using Generative Models
Jy-yong Sohn, Liang Shang, Hongxu Chen +3
Mixup is a data augmentation method that generates new data points by mixing a pair of input data. While mixup generally improves the prediction performance, it sometimes degrades…
Click-Through Rate Prediction with Multi-Modal Hypergraphs
Li He, Hongxu Chen, Dingxian Wang +3
Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One of the important signals that these platforms rely upon is the click-through rate (CTR) pr…