  Learning low-level node embeddings using techniques from network
representation learning is useful for solving downstream tasks such as node
classification and link prediction. An important consideration in such
applications is the robustness of the embedding algorithms against adversarial
attacks, which can be examined by performing perturbation on the original
network. An efficient perturbation technique can degrade the performance of
network embeddings on downstream tasks. In this paper, we study network
embedding algorithms from an adversarial point of view and observe the effect
of poisoning the network on downstream tasks. We propose VIKING, a supervised
network poisoning strategy that outperforms the state-of-the-art poisoning
methods by upto 18% on the original network structure. We also extend VIKING to
a semi-supervised attack setting and show that it is comparable to its
supervised counterpart.
