  Although link prediction on graphs has achieved great success with the
development of graph neural networks (GNNs), the potential robustness under the
edge noise is still less investigated. To close this gap, we first conduct an
empirical study to disclose that the edge noise bilaterally perturbs both input
topology and target label, yielding severe performance degradation and
representation collapse. To address this dilemma, we propose an
information-theory-guided principle, Robust Graph Information Bottleneck
(RGIB), to extract reliable supervision signals and avoid representation
collapse. Different from the basic information bottleneck, RGIB further
decouples and balances the mutual dependence among graph topology, target
labels, and representation, building new learning objectives for robust
representation against the bilateral noise. Two instantiations, RGIB-SSL and
RGIB-REP, are explored to leverage the merits of different methodologies, i.e.,
self-supervised learning and data reparameterization, for implicit and explicit
data denoising, respectively. Extensive experiments on six datasets and three
GNNs with diverse noisy scenarios verify the effectiveness of our RGIB
instantiations. The code is publicly available at:
https://github.com/tmlr-group/RGIB.
