[@wang2019dgcnn] proposes edge convolution to aggregate local features, which is applied to point cloud learning problem.

Graph Learning in GCNNs
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Graph Learning in GCNNs can be categorized into three classes based on the graph domain to address: 1) fixed-domain graph learning, in which the underlying graph is fixed; 2) varying-domain graph learning, where graphs vary across data instances; and 3) hybrid graph learning, which combines the former two classes.

### Fixed-domain Graph Learning

This class of graph learning assumes there is a shared graph structure underlying all instances with fixed vertices. In applications such as semi-supervised node classification of citation networks, there is only *one fixed* graph to be learned and shared by all the instances to classify. GLCN [@Jiang2018GraphLN] learns a non-negative function that represents the pairwise relationship between two vertices using attention mechanism via a single-layer neural network, and optimizes it by minimizing GLR in the loss function. Instead, our method models pairwise similarity more explicitly by adopting Mahalanobis distance, and we learn different graphs at different layers.

### Varying-domain Graph Learning

Instances corresponding to varying domains may require different graphs with possibly arbitrary number of vertices. Graph learning for such data applications learns adaptive graphs tailored 