暂无分享,去创建一个
[1] Xiao-Ming Wu,et al. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning , 2018, AAAI.
[2] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[4] Bernard Ghanem,et al. Can GCNs Go as Deep as CNNs? , 2019, ArXiv.
[5] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[6] Lise Getoor,et al. Collective Classification in Network Data , 2008, AI Mag..
[7] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[8] Tim Salimans,et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.
[9] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[10] Takanori Maehara,et al. Revisiting Graph Neural Networks: All We Have is Low-Pass Filters , 2019, ArXiv.
[11] Yoshua Bengio,et al. GMNN: Graph Markov Neural Networks , 2019, ICML.
[12] Kilian Q. Weinberger,et al. Simplifying Graph Convolutional Networks , 2019, ICML.
[13] Wenbing Huang,et al. The Truly Deep Graph Convolutional Networks for Node Classification , 2019, ArXiv.
[14] Stephan Günnemann,et al. Predict then Propagate: Combining neural networks with personalized pagerank for classification on graphs , 2018, ICLR 2018.
[15] Ken-ichi Kawarabayashi,et al. Representation Learning on Graphs with Jumping Knowledge Networks , 2018, ICML.
[16] Matthias Fey,et al. Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks , 2019, ArXiv.
[17] Stephan Günnemann,et al. Pitfalls of Graph Neural Network Evaluation , 2018, ArXiv.