Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph
  Neural Networks   Credit rating is an analysis of the credit risks associated with a
corporation, which reflects the level of the riskiness and reliability in
investing, and plays a vital role in financial risk. There have emerged many
studies that implement machine learning and deep learning techniques which are
based on vector space to deal with corporate credit rating. Recently,
considering the relations among enterprises such as loan guarantee network,
some graph-based models are applied in this field with the advent of graph
neural networks. But these existing models build networks between corporations
without taking the internal feature interactions into account. In this paper,
to overcome such problems, we propose a novel model, Corporate Credit Rating
via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual
graphs for each corporation based on self-outer product and then use GNN to
model the feature interaction explicitly, which includes both local and global
information. Extensive experiments conducted on the Chinese public-listed
corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art
methods consistently.

28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th