  A novel graph-to-tree conversion mechanism called the deep-tree generation
(DTG) algorithm is first proposed to predict text data represented by graphs.
The DTG method can generate a richer and more accurate representation for nodes
(or vertices) in graphs. It adds flexibility in exploring the vertex
neighborhood information to better reflect the second order proximity and
homophily equivalence in a graph. Then, a Deep-Tree Recursive Neural Network
(DTRNN) method is presented and used to classify vertices that contains text
data in graphs. To demonstrate the effectiveness of the DTRNN method, we apply
it to three real-world graph datasets and show that the DTRNN method
outperforms several state-of-the-art benchmarking methods.
