论文标题
朋友或人造：基于图表的社交网络上假帐户的早期检测
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks
论文作者
论文摘要
在本文中，我们仅根据与其他用户的网络连接，研究了在社交网络上早期发现假用户帐户的问题。删除此类帐户是维持社交网络完整性的核心任务，而早期检测有助于减少造成的危害。但是，众所周知，很难通过基于图的算法检测到新的假帐户，因为它们的少量连接不太可能反映出与新的真实帐户的连接差异很大。我们介绍了Sybilede算法，该算法通过汇总（i）她对朋友请求目标的选择以及（ii）这些目标的各自的响应来确定新用户是否为假帐户（`sybil'）。 Sybiledge执行此汇总，从而使用户选择目标的选择更大，以至于这些目标是其他假货与真实用户更喜欢的，并且这些目标对这些目标的响应方式与真实用户相比，对这些目标的响应方式不同。我们表明，Sybiledge在Facebook网络上迅速检测到新的虚假用户，并胜过最先进的算法。我们还表明，Sybiledge在培训数据中标记噪声，与网络中的伪造帐户的不同流行率一起标记噪声，并且在几种不同的方式上，Fakes可以为他们的朋友请求选择目标。据我们所知，这是第一次证明基于图的算法可以在只发送少量朋友请求的新用户上实现高性能（AUC> 0.9）。
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.
