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Anisotropic Graph Convolutional Network for Semi-supervised Learning
[article]
2020
arXiv
pre-print
Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as node classification. However, these networks suffer from the issue of over-smoothing and shrinking effect of the graph due in large part to the fact that they diffuse features across the edges of the graph using a linear Laplacian flow. This limitation is especially problematic for the task of node classification,
arXiv:2010.10284v1
fatcat:b3mjk6th2fajhlvx2clcseyfye