  Node embedding methods map network nodes to low dimensional vectors that can
be subsequently used in a variety of downstream prediction tasks. The
popularity of these methods has grown significantly in recent years, yet, their
robustness to perturbations of the input data is still poorly understood. In
this paper, we assess the empirical robustness of node embedding models to
random and adversarial poisoning attacks. Our systematic evaluation covers
representative embedding methods based on Skip-Gram, matrix factorization, and
deep neural networks. We compare edge addition, deletion and rewiring attacks
computed using network properties as well as node labels. We also investigate
the performance of popular node classification attack baselines that assume
full knowledge of the node labels. We report qualitative results via embedding
visualization and quantitative results in terms of downstream node
classification and network reconstruction performances. We find that node
classification results are impacted more than network reconstruction ones, that
degree-based and label-based attacks are on average the most damaging and that
label heterophily can strongly influence attack performance.
