  Dynamic Graphs widely exist in the real world, which carry complicated
spatial and temporal feature patterns, challenging their representation
learning. Dynamic Graph Neural Networks (DGNNs) have shown impressive
predictive abilities by exploiting the intrinsic dynamics. However, DGNNs
exhibit limited robustness, prone to adversarial attacks. This paper presents
the novel Dynamic Graph Information Bottleneck (DGIB) framework to learn robust
and discriminative representations. Leveraged by the Information Bottleneck
(IB) principle, we first propose the expected optimal representations should
satisfy the Minimal-Sufficient-Consensual (MSC) Condition. To compress
redundant as well as conserve meritorious information into latent
representation, DGIB iteratively directs and refines the structural and feature
information flow passing through graph snapshots. To meet the MSC Condition, we
decompose the overall IB objectives into DGIB$_{MS}$ and DGIB$_C$, in which the
DGIB$_{MS}$ channel aims to learn the minimal and sufficient representations,
with the DGIB$_{MS}$ channel guarantees the predictive consensus. Extensive
experiments on real-world and synthetic dynamic graph datasets demonstrate the
superior robustness of DGIB against adversarial attacks compared with
state-of-the-art baselines in the link prediction task. To the best of our
knowledge, DGIB is the first work to learn robust representations of dynamic
graphs grounded in the information-theoretic IB principle.
