Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Label-informed Graph Structure Learning for Node Classification   Graph Neural Networks (GNNs) have achieved great success among various
domains. Nevertheless, most GNN methods are sensitive to the quality of graph
structures. To tackle this problem, some studies exploit different graph
structure learning strategies to refine the original graph structure. However,
these methods only consider feature information while ignoring available label
information. In this paper, we propose a novel label-informed graph structure
learning framework which incorporates label information explicitly through a
class transition matrix. We conduct extensive experiments on seven node
classification benchmark datasets and the results show that our method
outperforms or matches the state-of-the-art baselines.

18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th