  Compared to sequential learning models, graph-based neural networks exhibit
some excellent properties, such as ability capturing global information. In
this paper, we investigate graph-based neural networks for text classification
problem. A new framework TensorGCN (tensor graph convolutional networks), is
presented for this task. A text graph tensor is firstly constructed to describe
semantic, syntactic, and sequential contextual information. Then, two kinds of
propagation learning perform on the text graph tensor. The first is intra-graph
propagation used for aggregating information from neighborhood nodes in a
single graph. The second is inter-graph propagation used for harmonizing
heterogeneous information between graphs. Extensive experiments are conducted
on benchmark datasets, and the results illustrate the effectiveness of our
proposed framework. Our proposed TensorGCN presents an effective way to
harmonize and integrate heterogeneous information from different kinds of
graphs.
