朋友还是虚假：基于图的社交网络虚假账户早期检测
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks
摘要：
在本文中，我们研究的问题，早期检测虚假的用户帐户在社交网络上的基础上，完全与其他用户的网络连接。删除此类账户是维护社交网络完整性的核心任务，及早发现有助于减少此类账户造成的危害。然而，新的虚假账户很难通过基于图的算法检测到，因为它们的连接数量很少，不太可能反映出与新的真实账户的结构差异。我们提出了SybilEdge算法，它确定一个新用户是否是一个假帐户（“sybil”）通过聚合（I）她的朋友请求目标的选择和（II）这些目标的相应响应。SybilEdge执行这种聚合，为用户对目标的选择赋予更多权重，以使这些目标受到其他假冒用户与真实用户的偏好，并且还使这些目标对假冒用户与真实用户的响应不同。我们发现，SybilEdge可以快速检测Facebook网络上大规模的新虚假用户，并且性能优于最先进的算法。我们还表明，SybilEdge是强大的标签噪声的训练数据，不同的流行假帐户在网络中，和几种不同的方式假货可以选择他们的朋友请求的目标。据我们所知，这是第一次基于图的算法被证明在只发送少量好友请求的新用户上实现高性能（AUC>0.9）。
英文摘要：
In this paper, we study the problem of early detection of fake user accounts on social networks based solely on their network connectivity with other users. Removing such accounts is a core task for maintaining the integrity of social networks, and early detection helps to reduce the harm that such accounts inflict. However, new fake accounts are notoriously difficult to detect via graph-based algorithms, as their small number of connections are unlikely to reflect a significant structural difference from those of new real accounts. We present the SybilEdge algorithm, which determines whether a new user is a fake account (`sybil') by aggregating over (I) her choices of friend request targets and (II) these targets' respective responses. SybilEdge performs this aggregation giving more weight to a user's choices of targets to the extent that these targets are preferred by other fakes versus real users, and also to the extent that these targets respond differently to fakes versus real users. We show that SybilEdge rapidly detects new fake users at scale on the Facebook network and outperforms state-of-the-art algorithms. We also show that SybilEdge is robust to label noise in the training data, to different prevalences of fake accounts in the network, and to several different ways fakes can select targets for their friend requests. To our knowledge, this is the first time a graph-based algorithm has been shown to achieve high performance (AUC>0.9) on new users who have only sent a small number of friend requests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
