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 significantly increased 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 strategies computed using network properties as well as node labels. We also investigate the effect of label homophily and heterophily on robustness. We report qualitative results via embedding visualization and quantitative results in terms of downstream node classification and network reconstruction performances. We found that node classification suffers from higher performance degradation as opposed to network reconstruction, and that degree-based and label-based attacks are on average the most damaging.
翻译:节点嵌入方法 映射低维矢量的网络节点,这些节点随后可用于各种下游预测任务。近年来,这些方法的普及程度显著提高,然而,这些方法对干扰输入数据的能力仍然不甚了解。在本文件中,我们评估了节点嵌入模式对随机和对抗性中毒袭击的经验强度。我们的系统评估涵盖基于跳格格、矩阵因子化和深神经网络的代表性嵌入方法。我们比较了利用网络属性和节点标签计算出的边端添加、删除和重新连线战略。我们还调查了标签单词和杂交对稳健性的影响。我们通过嵌入下游节点分类和网络重建性能方面的可视化和定量结果报告质量结果。我们发现,节点分类与网络重建相比,性能退化程度较高,而且基于度和标签的攻击平均损害最大。