  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.
