In this paper, we describe a new algorithm called Preferential Attachment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorithms have obtained high accuracy on this problem. However, they have done so by relying on information about fake accounts' friendships or the content they share with others--the very things we seek to prevent. PreAttacK represents a significant departure from these approaches. We provide some of the first detailed distributional analyses of how new fake (and real) accounts first attempt to request friends after joining a major network (Facebook). We show that even before a new account has made friends or shared content, these initial friend request behaviors evoke a natural multi-class extension of the canonical Preferential Attachment model of social network growth. We use this model to derive a new algorithm, PreAttacK. We prove that in relevant problem instances, PreAttacK near-optimally approximates the posterior probability that a new account is fake under this multi-class Preferential Attachment model of new accounts' (not-yet-answered) friend requests. These are the first provable guarantees for fake account detection that apply to new users, and that do not require strong homophily assumptions. This principled approach also makes PreAttacK the only algorithm with provable guarantees that obtains state-of-the-art performance on new users on the global Facebook network, where it converges to AUC=0.9 after new users send + receive a total of just 20 not-yet-answered friend requests. For comparison, state-of-the-art benchmarks do not obtain this AUC even after observing additional data on new users' first 100 friend requests. Thus, unlike mainstream algorithms, PreAttacK converges before the median new fake account has made a single friendship (accepted friend request) with a human.

翻译：本文描述了一种名为“优先连接k类分类器”（PreAttacK）的新算法，用于检测社交网络中的虚假账户。近年来，多种算法在该问题上取得了高准确率，但这些算法依赖虚假账户的好友关系或与他人分享的内容信息，而这正是我们试图预防的。PreAttacK与此类方法截然不同。我们首次对新的虚假（及真实）账户在加入主要网络（如Facebook）后最初尝试发送好友请求的行为进行了详细的分布分析。研究表明，即使在账户尚未建立好友或分享内容之前，这些初始好友请求行为便自然地引出了社交网络增长中经典优先连接模型的多类扩展。基于此模型，我们推导出新算法PreAttacK。我们证明，在相关的问题实例中，PreAttacK能够近最优地近似逼近新账户在此多类优先连接模型（针对尚未回复的好友请求）下属于虚假账户的后验概率。这是首个适用于新用户且无需强同质性假设的虚假账户检测可证保证。这一基于原理的方法也使PreAttacK成为唯一在全球Facebook网络上对新用户获得性能最优且具有可证保证的算法：当新用户发送及接收总计仅20个尚未回复的好友请求时，其AUC即可收敛至0.9。相比之下，最先进的基准算法即使观察新用户前100个好友请求的额外数据，也无法达到这一AUC。因此，与主流算法不同，PreAttacK在新的虚假账户与人类完成第一个好友关系（即接受好友请求）之前便已收敛。