Learning Graph Pooling and Hybrid Convolutional Operations for Text
  Representations
  With the development of graph convolutional networks (GCN), deep learning
methods have started to be used on graph data. In additional to convolutional
layers, pooling layers are another important components of deep learning.
However, no effective pooling methods have been developed for graphs currently.
In this work, we propose the graph pooling (gPool) layer, which employs a
trainable projection vector to measure the importance of nodes in graphs. By
selecting the k-most important nodes to form the new graph, gPool achieves the
same objective as regular max pooling layers operating on images. Another
limitation of GCN when used on graph-based text representation tasks is that,
GCNs do not consider the order information of nodes in graph. To address this
limitation, we propose the hybrid convolutional (hConv) layer that combines GCN
and regular convolutional operations. The hConv layer is capable of increasing
receptive fields quickly and computing features automatically. Based on the
proposed gPool and hConv layers, we develop new deep networks for text
categorization tasks. Our results show that the networks based on gPool and
hConv layers achieves new state-of-the-art performance as compared to baseline
methods.
