聚类对网络去匿名化性能的影响
Impact of Clustering on the Performance of Network De-anonymization
摘要：
最近，图匹配算法已被成功地应用到网络去匿名化的问题，其中节点（用户）参与到一个以上的社会网络被识别，只有通过他们的链接到其他成员的结构。这个过程利用了一组初始的种子节点，这些节点足够大，可以触发一个渗透过程，这个过程可以正确地匹配不同社交网络中几乎所有的其他节点。我们的主要贡献是展示聚类所发挥的关键作用，这是现实社会网络图（和许多其他系统）的一个无处不在的功能。聚类既有使匹配算法更容易出错的效果，也有可能大大减少触发渗透所需的种子数量，这要归功于波浪式传播效应。我们证明了这些事实，考虑一个相当普遍的类的随机几何图形与可变聚类水平，并显示如何聪明的算法可以实现令人惊讶的良好性能，同时包含匹配错误。
英文摘要：
Recently, graph matching algorithms have been successfully applied to the problem of network de-anonymization, in which nodes (users) participating to more than one social network are identified only by means of the structure of their links to other members. This procedure exploits an initial set of seed nodes large enough to trigger a percolation process which correctly matches almost all other nodes across the different social networks. Our main contribution is to show the crucial role played by clustering, which is a ubiquitous feature of realistic social network graphs (and many other systems). Clustering has both the effect of making matching algorithms more vulnerable to errors, and the potential to dramatically reduce the number of seeds needed to trigger percolation, thanks to a wave-like propagation effect. We demonstrate these facts by considering a fairly general class of random geometric graphs with variable clustering level, and showing how clever algorithms can achieve surprisingly good performance while containing matching errors.
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