An Experimental Study of Neural Networks for Variable Graphs

By Xavier Bresson, Thomas Laurent. 2018

44 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Guohao Li
2. Dinh Van Tran
3. Nicoló Navarin
4. Bernard Ghanem
5. Ali K. Thabet
6. Alessandro Sperduti
7. Chenxin Xiong
8. Jianwen Xie

Citations in the corpus

• Pre-training Graph Neural Networks with Kernels (2018, positive): “However, recently it has been shown in Bresson and Laurent [2018] that stacked graph convolutions are superior to graph recurrent architectures in terms of both accuracy and computational cost.”
• DeeperGCN: All You Need to Train Deeper GCNs (2020, methodology): “The methods include GCN [Kipf and Welling, 2016], GraphSAGE [Hamilton et al., 2017], GIN [Xu et al., 2019b], GIN with virtual nodes, GaAN [Zhang et al., 2018], and GatedGCN [Bresson and Laurent, 2018].”
• On Filter Size in Graph Convolutional Networks (2018, background): “However, recently it has been shown in [12] that stacked graph convolutions are superior to graph recurrent architectures in terms of both accuracy and computational cost.”