Title: Data Poisoning Attack against Unsupervised Node Embedding Methods,
Abstract: Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have
attracted growing interests given their simplicity and effectiveness. However,
although these methods have been proved effective in a variety of applications,
none of the existing work has analyzed the robustness of them. This could be
very risky if these methods are attacked by an adversarial party. In this
paper, we take the task of link prediction as an example, which is one of the
most fundamental problems for graph analysis, and introduce a data positioning
attack to node embedding methods. We give a complete characterization of
attacker's utilities and present efficient solutions to adversarial attacks for
two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed
attack model on multiple real-world graphs. Experimental results show that our
proposed model can significantly affect the results of link prediction by
slightly changing the graph structures (e.g., adding or removing a few edges).
We also show that our proposed model is very general and can be transferable
across different embedding methods. Finally, we conduct a case study on a
coauthor network to better understand our attack method.