is available.

### Datasets

We test on ModelNet40 dataset for point cloud classification. ModelNet40 includes 12,311 CAD models from 40 man-made categories, which are split into 9,843 for training and 2,468 for testing. As in previous work [@qi2017pointnet; @qi2017pointnet++; @wang2019dgcnn], we uniformly sample 1024 points on mesh faces according to face area and normalize them into a unit sphere, where only coordinates of points are employed as input features.

### Experimental settings

We stack 3 JLGCN layers to extract point cloud features as well as learn the underlying graph without any ground truth graph. The hidden units for each layer is {64, 128, 1024}. After extracting point cloud features, we deploys a permutation-invariant graph max-pooling to generate a global context feature for the entire point cloud, and employs a fully connected network with hidden units of {512, 256, 40} to classify the point cloud. The negative slope of leaky ReLU activation is set to 0.2 and dropout with $p=0.5$ is used in the fully connected classification network. We train the network with a learning rate of 0.001 for 400 epochs and decay it by 0.5 every 40 epochs using ADAM. The batch size is set to 32, weight decay to 