Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age, location, education, interests, and others. The task of matching user identities across different social networks is considered a challenging task. In this work, we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data, i.e., user-name and friendship. Thus, we propose a framework, ExpandUIL, that includes three standalone algorithms based on (i) the percolation graph matching in ExpandFullName algorithm, (ii) a supervised machine learning algorithm that works with the graph embedding, and (iii) a combination of the two, ExpandUserLinkage algorithm. The proposed framework as a set of algorithms is significant as, (i) it is based on the network topology and requires only name feature of the nodes, (ii) it requires a considerably low initial seed, as low as one initial seed suffices, (iii) it is iterative and scalable with applicability to online incoming stream graphs, and (iv) it has an experimental proof of stability over a real ground-truth dataset. Experiments on real datasets, Instagram and VK social networks, show upto 75% recall for linked accounts with 96% accuracy using only one given seed pair.

中文翻译：

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使用有限的个人资料数据在整个社交网络上匹配用户身份

隐私保护是社交网络中的一个主要问题，社交网络采用各种隐私保护机制来保存和保护敏感的用户信息，包括年龄，位置，教育程度，兴趣等。跨不同社交网络匹配用户身份的任务被认为是一项具有挑战性的任务。在这项工作中，我们提出了一种算法，该算法使用有限的用户配置文件数据（即用户名和友谊）将用户身份显示为来自不同社交网络的一组关联帐户。因此，我们提出了一个框架ExpandUIL，该框架包括以下三个独立的算法，它们基于（i）ExpandFullName算法中的渗流图匹配，（ii）与图嵌入配合使用的有监督的机器学习算法，以及（iii）二，ExpandUserLinkage算法。提议的框架作为一组算法非常重要，因为（i）基于网络拓扑，并且仅要求节点的名称特征；（ii）需要相当低的初始种子，低至一个初始种子就足够了， （iii）具有迭代性和可扩展性，适用于在线传入流图，并且（iv）在真实地面数据集上具有稳定性的实验证明。在真实数据集，Instagram和VK社交网络上进行的实验显示，仅使用一个给定的种子对，关联帐户的召回率高达75％，准确率达到96％。（iv）在真实地面数据集上具有稳定性的实验证明。在真实数据集，Instagram和VK社交网络上进行的实验显示，仅使用一个给定的种子对，关联帐户的召回率高达75％，准确率达到96％。（iv）在真实地面数据集上具有稳定性的实验证明。在真实数据集，Instagram和VK社交网络上进行的实验显示，仅使用一个给定的种子对，关联帐户的召回率高达75％，准确率达到96％。