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.

本文研究了无监督节点嵌入方法（如 DeepWalk，LINE 和 node2vec）的鲁棒性问题，提出了一种数据定位攻击来攻击这些方法，针对两种流行的节点嵌入方法（DeepWalk 和 LINE）提出了有效的解决方案，并在多个真实图上评估了攻击模型的性能