extracted by alternatively learning an optimal graph based on features and propagating features based on the learned graph, which are then fed into the classification module for label prediction.

### Node classification configuration

We stack multiple JLGCN layers and deploy leaky ReLU activation [@xu2015empirical]. Dropout is applied to the last layer’s input to reduce over-fitting. Features from the last layer is then employed for prediction of labels at each node.

### Graph classification configuration

We stack multiple JLGCN layers followed by batch normalization [@ioffe2015batch] and leaky ReLU activation. Graph max pooling is applied to features from the last layer, which are then fed into a fully connected network for the classification of the entire graph.

Further, we perform feature concatenation at each layer $l$ with $\mathbf{f}_{l-1}$ from the previous layer for multi-level feature learning to enhance the model capacity. This leads to $$\mathbf{f}_{l} = (\mathbf{f}_{l-1} || \mathbf{A}_l \mathbf{f}_{l-1}) \mathbf{W}_l,
\label{eq:gcn_concat}$$ where $||$ is the concatenation operator in feature dimension. This essentially assigns more weighting to each node’s own feature when no ground truth graph is available and accelerates the convergence.

Experimental Results {#sec:results}
====================

In order to evaluate the performance of JLGCN, we apply it to the problems of semi-supervised 