Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Long-Range Performance of Gated Graph Neural Networks   Many popular variants of graph neural networks (GNNs) that are capable of
handling multi-relational graphs may suffer from vanishing gradients. In this
work, we propose a novel GNN architecture based on the Gated Graph Neural
Network with an improved ability to handle long-range dependencies in
multi-relational graphs. An experimental analysis on different synthetic tasks
demonstrates that the proposed architecture outperforms several popular GNN
models.

2
The categories of this paper are: math
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int