论文标题
在线社交网络中检测自动托管帐户：图形嵌入方法
Detecting Automatically Managed Accounts in Online Social Networks: Graph Embedding Approach
论文作者
论文摘要
在线社交网络的广泛广泛传播以及对流行帐户进行商业化的机会吸引了大量自动化程序，称为人工帐户。本文通过采用多个图形神经网络，有效地编码帐户的属性和网络图来，重点介绍社交网络上人类和假帐户的分类。我们的工作同时使用网络结构和属性来区分人类和人工账户，并比较归因于传统的图形嵌入。将复杂的，类人类的人造帐户分为独立的任务显示出基于配置文件的算法用于机器人检测的显着局限性，并显示了基于网络结构的方法检测复杂机器人帐户的效率。实验表明，与仅具有网络驱动功能的现有最新机器人检测系统相比，我们的方法可以实现竞争性能。本文的源代码可在以下网址获得：http：//github.com/karpovilia/botdetection。
The widespread of Online Social Networks and the opportunity to commercialize popular accounts have attracted a large number of automated programs, known as artificial accounts. This paper focuses on the classification of human and fake accounts on the social network, by employing several graph neural networks, to efficiently encode attributes and network graph features of the account. Our work uses both network structure and attributes to distinguish human and artificial accounts and compares attributed and traditional graph embeddings. Separating complex, human-like artificial accounts into a standalone task demonstrates significant limitations of profile-based algorithms for bot detection and shows the efficiency of network structure-based methods for detecting sophisticated bot accounts. Experiments show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems with only network-driven features. The source code of this paper is available at: http://github.com/karpovilia/botdetection.
