Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topology Attack and Defense for Graph Neural Networks: An Optimization
  Perspective   Graph neural networks (GNNs) which apply the deep neural networks to graph
data have achieved significant performance for the task of semi-supervised node
classification. However, only few work has addressed the adversarial robustness
of GNNs. In this paper, we first present a novel gradient-based attack method
that facilitates the difficulty of tackling discrete graph data. When comparing
to current adversarial attacks on GNNs, the results show that by only
perturbing a small number of edge perturbations, including addition and
deletion, our optimization-based attack can lead to a noticeable decrease in
classification performance. Moreover, leveraging our gradient-based attack, we
propose the first optimization-based adversarial training for GNNs. Our method
yields higher robustness against both different gradient based and greedy
attack methods without sacrificing classification accuracy on original graph.

4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int