Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainability in Graph Neural Networks: An Experimental Survey   Graph neural networks (GNNs) have been extensively developed for graph
representation learning in various application domains. However, similar to all
other neural networks models, GNNs suffer from the black-box problem as people
cannot understand the mechanism underlying them. To solve this problem, several
GNN explainability methods have been proposed to explain the decisions made by
GNNs. In this survey, we give an overview of the state-of-the-art GNN
explainability methods and how they are evaluated. Furthermore, we propose a
new evaluation metric and conduct thorough experiments to compare GNN
explainability methods on real world datasets. We also suggest future
directions for GNN explainability.

37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an