  In network link prediction, it is possible to hide a target link from being
predicted with a small perturbation on network structure. This observation may
be exploited in many real world scenarios, for example, to preserve privacy, or
to exploit financial security. There have been many recent studies to generate
adversarial examples to mislead deep learning models on graph data. However,
none of the previous work has considered the dynamic nature of real-world
systems. In this work, we present the first study of adversarial attack on
dynamic network link prediction (DNLP). The proposed attack method, namely
time-aware gradient attack (TGA), utilizes the gradient information generated
by deep dynamic network embedding (DDNE) across different snapshots to rewire a
few links, so as to make DDNE fail to predict target links. We implement TGA in
two ways: one is based on traversal search, namely TGA-Tra; and the other is
simplified with greedy search for efficiency, namely TGA-Gre. We conduct
comprehensive experiments which show the outstanding performance of TGA in
attacking DNLP algorithms.
