3 citations · 5 across the 6 of their papers we have counts for
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cs.LG2023★ 2 cited
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…
cs.LG2023
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
Qiang Huang, Jiawei Jiang, Xi Susie Rao +10
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGN…