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GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-Wise Transformations
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with high cost. It it thus critical to learn graph feature representations in an unsupervised manner in practice. To this end, we propose a novel unsupervised learning of Graph Transformation Equivariant Representations (GraphTER), aiming to capture intrinsic patterns of graph structure under both global and local
doi:10.1109/cvpr42600.2020.00719
dblp:conf/cvpr/GaoHQ20
fatcat:227op5s6wnck7ff5wupmnykysu