and learn different graphs at different layers jointly with node features from data.

Secondly, we evaluate the performance of JLGCN with different $\lambda$—the weighting parameter of GLR in the loss function—with respect to different edge missing ratios. As listed in Tab. \[tab:ablation\], we see that JLGCN achieves the highest accuracy when $\lambda$ is set to 0.0001. Also, GLR helps improve the performance especially when the ground truth graph is incomplete or even unavailable.

### Visualization of the learned graph

We also visualize the learned graph at each layer in Fig. \[fig:visual\_cora\], and compare them with the ground truth graph. The value of the matrix is log-transformed to make it more interpretable. Note that the learned graph is both dense and weighted, which not only adds self-connection to the unweighted ground truth graph as the original GCN does, but also extracts additional hidden connections between similar nodes. Also, the learned graph at the second layer puts higher weighting to self-connection compared to the first layer, showing that different layers correspond to varying graphs as we assumed.

Point cloud classification
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Furthermore, we test our model on point cloud classification to validate the effectiveness of our model when no ground truth graph 