Search Results for author: Chengxuan Ying

Found 4 papers, 2 papers with code

Do Transformers Really Perform Badly for Graph Representation?

no code implementations NeurIPS 2021 Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu

Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.

Graph Representation Learning

First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track

4 code implementations15 Jun 2021 Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, Di He

In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track.

Do Transformers Really Perform Bad for Graph Representation?

4 code implementations9 Jun 2021 Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu

Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.

Graph Classification Graph Property Prediction +2

LazyFormer: Self Attention with Lazy Update

no code implementations25 Feb 2021 Chengxuan Ying, Guolin Ke, Di He, Tie-Yan Liu

In each lazy block, the self-attention distribution is only computed once in the first layer and then is reused in all upper layers.

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