  The goal of network representation learning is to learn low-dimensional node
embeddings that capture the graph structure and are useful for solving
downstream tasks. However, despite the proliferation of such methods, there is
currently no study of their robustness to adversarial attacks. We provide the
first adversarial vulnerability analysis on the widely used family of methods
based on random walks. We derive efficient adversarial perturbations that
poison the network structure and have a negative effect on both the quality of
the embeddings and the downstream tasks. We further show that our attacks are
transferable since they generalize to many models and are successful even when
the attacker is restricted.
