Detecting Automatically Managed Accounts in Online Social Networks: Graph Embeddings Approach

P. 11–21.

Karpov I., Glazkova E.

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 (Project repository available at http://github.com/karpovilia/botdetection) 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 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.

Keywords: social network analysis, random walk, Node2vec, Graph Embeddings, Bot detection, Attri2Vec

Publication based on the results of: Applied Network Analysis for the Solution of Current Problems of the State, Business and Society: Methodological Developments and Practical Implementations (2022)

In book

Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020 Revised Supplementary Proceedings

Vol. 12602. , Springer, 2021.