Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Robust Graph Contrastive Learning   We study the problem of adversarially robust self-supervised learning on
graphs. In the contrastive learning framework, we introduce a new method that
increases the adversarial robustness of the learned representations through i)
adversarial transformations and ii) transformations that not only remove but
also insert edges. We evaluate the learned representations in a preliminary set
of experiments, obtaining promising results. We believe this work takes an
important step towards incorporating robustness as a viable auxiliary task in
graph contrastive learning.

3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio