294 citations · 707 across the 54 of their papers we have counts for
8 papers · 1 filter
Git: Clustering Based on Graph of Intensity Topology
Zhangyang Gao, Haitao Lin, Cheng Tan +2
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clusteri…
An Empirical Study: Extensive Deep Temporal Point Process
Haitao Lin, Cheng Tan, Lirong Wu +3
Temporal point process as the stochastic process on continuous domain of time is commonly used to model the asynchronous event sequence featuring with occurrence timestamps. Thanks…
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Jun Xia, Lirong Wu, Ge Wang +2
Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive s…
Co-learning: Learning from Noisy Labels with Self-supervision
Cheng Tan, Jun Xia, Lirong Wu +1
Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degra…
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on the assumption that node…
Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Deep learning on graphs has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the massive and carefully labeled data. However, precis…