Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph-based Deep-Tree Recursive Neural Network (DTRNN) for Text
  Classification   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.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th