- GRAPH CONVOLUTIONAL NETWORKS (by THOMAS KIPF)
- How powerful are Graph Convolutions? (review of Kipf & Welling, 2016) (by inFERENCe)
- paper: Li, G. , Müller, M. , Thabet A, , & Ghanem B. . (2019). DeepGCNs: Can GCNs Go as Deep as CNNs?
- paper: Chen, J. , Ma, T. , & Xiao, C. . (2018). FastGCN: fast learning with graph convolutional networks via importance sampling.
- paper: Zhang, Z. , Cui, P. , & Zhu, W. . (2018). Deep Learning on Graphs: A Survey.
- paper: Bruna, J. , Zaremba, W. , Szlam, A. , & Lecun, Y. . (2013). Spectral networks and locally connected networks on graphs.
- link: https://github.com/tkipf/pygcn
- author: Thomas Kipf
- paper: Kipf, T. N. , & Welling, M. . (2016). Semi-supervised classification with graph convolutional networks.
- note: a PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification
- extra: the tensorflow version from the same author.