In today's networked society, many real-world problems can be formalized as predicting links in networks, such as Facebook friendship suggestions, e-commerce recommendations, and the prediction of scientific collaborations in citation networks. Increasingly often, link prediction problem is tackled by means of network embedding methods, owing to their state-of-the-art performance. However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model? Prior research has already investigated adversarial robustness for network embedding models, focused on classification at the node and graph level. Robustness with respect to the link prediction downstream task, on the other hand, has been explored much less. This paper contributes to filling this gap, by studying adversarial robustness of Conditional Network Embedding (CNE), a state-of-the-art probabilistic network embedding model, for link prediction. More specifically, given CNE and a network, we measure the sensitivity of the link predictions of the model to small adversarial perturbations of the network, namely changes of the link status of a node pair. Thus, our approach allows one to identify the links and non-links in the network that are most vulnerable to such perturbations, for further investigation by an analyst. We analyze the characteristics of the most and least sensitive perturbations, and empirically confirm that our approach not only succeeds in identifying the most vulnerable links and non-links, but also that it does so in a time-efficient manner thanks to an effective approximation.
翻译:在当今的网络化社会中,许多现实世界的问题可以正式化,如预测网络的网络联系,如Facebook友谊建议、电子商务建议和预测引用网络的科学协作。越来越多的情况是,通过网络嵌入方法解决了预测问题,因为其表现最先进的是网络嵌入方法。然而,与简单的基线相比,这些方法缺乏透明度,因此,它们对于对抗性攻击的强力可能是一个令人关切的问题:在使用网络嵌入模型时,对网络的一小小点对抗性修改能否对链接预测绩效产生很大影响?以前的研究已经调查了网络嵌入模型的对抗性强力关系,重点是节点和图表层次的分类。另一方面,对网络嵌入模型的强势性进行了研究。 本文有助于填补这一差距,研究了Condal 网络嵌入(CNE)的强力对抗性对抗性强力,对网络嵌入模型的状态和最不稳定性稳定性网络的稳定性进行确认,用于连接预测。 更具体地说,CNE和大多数网络的不可靠性联系,我们测量了网络的敏感性联系,从而可以对网络进行预测。