entropy along with node features.

-   Extensive experiments on semi-supervised learning and point cloud classification demonstrate the superiority and robustness of our method.

Related Work {#sec:related}
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Graph Convolution Neural Networks
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As a generalization of CNNs to irregular graph domain, GCNNs can be categorized into two main classes: spectral methods and spatial methods.

### Spectral methods

This class of methods define graph convolution based on the spectral representation of graphs. [@bruna2013spectral] defines the convolution in graph Fourier transform domain by eigen-decomposition of the graph Laplacian, but requires intense computations. [@defferrard2016convolutional] addresses this problem by leveraging Chebyshev polynomials to approximate spectral filters and achieves localized filtering. GCN [@Kipf2016SemiSupervisedCW] further simplifies the previous work by employing only first-order approximation of the filters. Our proposed joint learning of graphs and node features is based on GCN, which has spectral interpretation and scales linearly with the number of edges.

### Spatial methods

Instead of spectral representation, spatial methods define graph convolution directly on each node and its neighbors for feature propagation. Mixture model network [@monti2017geometric] provides a unified generalization of CNN architectures on graphs. Graph attention network (GAT) in [@velivckovic2017graph] employs self-attention mechanism to solve the node classification problem on citation networks. DGCNN 