leaky ReLU activation with negative slope $\alpha=0.2$ between two layers. We train our JLGCN for a maximum of 500 epochs using ADAM algorithm [@kingma2014adam] with a learning rate of 0.1 and weight decay of 0.0005. The learning rate is decayed by 0.5 every 100 epochs. The hyper parameter $\lambda$ for GLR is set to 0.0001. We report the average classification accuracy of 10 runs with different random seeds.

### Baselines

We compare our JLGCN against the baseline of GCN [@Kipf2016SemiSupervisedCW] and Planetoid [@yang2016revisiting], and also against some other graph neural network based semi-supervised learning methods, including Graph Attention Network (GAT) [@velivckovic2017graph] and Graph Markov Neural Network (GMNN) [@qu2019gmnn]. These methods use the same dataset partition as in our method for fair comparison with available codes.

### Results

Tab. \[tab:results\_citation\] shows the comparison results on three citation network datasets. The best results are marked in bold. Overall, we note that 1) JLGCN outperforms the GCN baseline on all datasets significantly by only adding a graph learning module to each layer. This clearly demonstrates the effectiveness of jointly learning graphs and node features. Compared to the unweighted ground truth graph used in GCN, our learned graphs (as shown in Fig. \[fig:visual\_cora\]) enhance 