evaluate; 2) we test the robustness of our model to incomplete ground truth graph, *i.e.*, we randomly drop out edges in the ground truth graph, with edge missing ratios set as {0, 0.25, 0.5, 0.75, 0.9, 1.0}. When the edge missing ratio is 1.0, an empty ground truth graph is fed into the network. Since randomly initialized graph is hard to converge, we set $S=K$ for the transformation matrix $\mathbf{R}$ and initialize it as an identity matrix in this case. Fig. \[fig:robust\_citation\] shows our model significantly outperforms GCN and GAT, especially when the edge missing ratio is high. This validates the superiority of our learned graph structure in terms of robustness.

### Ablation study

We first examine the effectiveness of the graph learning module on Cora dataset. Specifically, we compare with 1) GCN, where the ground truth graph is employed; 2) GCN + Euclidean, where we replace the proposed graph learning with manual graph construction using Euclidean distance metric to compute edge weights; 3) JLGCN without dynamic updating of graphs, *i.e.*, we only learn one graph from the input data feature. As reported in Tab. \[tab:ablation\], our method leads to the best result when we adopt the Mahalanobis distance metric 