arXiv
Open Access
2021
Towards Robust Graph Contrastive Learning
Nikola Jovanović
Zhao Meng
Lukas Faber
Roger Wattenhofer
Abstrak
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.
Penulis (4)
N
Nikola Jovanović
Z
Zhao Meng
L
Lukas Faber
R
Roger Wattenhofer
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2021
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓