19 citations · 68 across the 11 of their papers we have counts for
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cs.LG2023★ 2 cited
BeMap: Balanced Message Passing for Fair Graph Neural Network
Xiao Lin, Jian Kang, Weilin Cong +1
Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplify…
cs.LG2023★ 19 cited
Do We Really Need Complicated Model Architectures For Temporal Networks?
Weilin Cong, Si Zhang, Jian Kang +5
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we fo…
cs.LG2023★ 1 cited
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
Weilin Cong, Mehrdad Mahdavi
As privacy protection receives much attention, unlearning the effect of a specific node from a pre-trained graph learning model has become equally important. However, due to the no…