GCNII
0 订阅
GCNII is an extension of a Graph Convolution Networks with two new techniques, initial residual and identify mapping, to tackle the problem of oversmoothing -- where stacking more layers and adding non-linearity tends to degrade performance. At each layer, initial residual constructs a skip connection from the input layer, while identity mapping adds an identity matrix to the weight matrix.
相关学科: AdaGPRS-GCNGATGCNGraph ClassificationGraph AttentionNode ClassificationKnowledge DistillationLink PredictionConvolution
学科讨论

暂无讨论内容,你可以
推荐文献
按被引用数
学科管理组
暂无学科课代表,你可以申请成为课代表
重要学者
Chuan Shi
4731 被引用,212
篇论文
Yaliang Li
2704 被引用,142
篇论文
Cheng Yang
2585 被引用,60
篇论文
Zhewei Wei
1037 被引用,68
篇论文
Zengfeng Huang
665 被引用,68
篇论文
Bolin Ding
509 被引用,131
篇论文
Ming Chen
196 被引用,6
篇论文
Jiawei Liu
10 被引用,2
篇论文