  Graph convolutional networks (GCNs) have been widely used for representation
learning on graph data, which can capture structural patterns on a graph via
specifically designed convolution and readout operations. In many graph
classification applications, GCN-based approaches have outperformed traditional
methods. However, most of the existing GCNs are inefficient to preserve local
information of graphs -- a limitation that is especially problematic for graph
classification. In this work, we propose a locality-preserving dense GCN with
graph context-aware node representations. Specifically, our proposed model
incorporates a local node feature reconstruction module to preserve initial
node features into node representations, which is realized via a simple but
effective encoder-decoder mechanism. To capture local structural patterns in
neighbourhoods representing different ranges of locality, dense connectivity is
introduced to connect each convolutional layer and its corresponding readout
with all previous convolutional layers. To enhance node representativeness, the
output of each convolutional layer is concatenated with the output of the
previous layer's readout to form a global context-aware node representation. In
addition, a self-attention module is introduced to aggregate layer-wise
representations to form the final representation. Experiments on benchmark
datasets demonstrate the superiority of the proposed model over
state-of-the-art methods in terms of classification accuracy.
