Detecting anomalies in graph networks on digital markets.

ABSTRACT: The study examines different graph-based methods of detecting anomalous activities on digital markets, proposing the most efficient way to increase market actors' protection and reduce information asymmetry. Anomalies are defined below as both bots and fraudulent users (who can be both bots and real people). Methods are compared against each other, and state-of-the-art results from the literature and a new algorithm is proposed. The goal is to find an efficient method suitable for threat detection, both in terms of predictive performance and computational efficiency. It should scale well and remain robust on the advancements of the newest technologies. The article utilized three publicly accessible graph-based datasets: one describing the Twitter social network (TwiBot-20) and two describi

SUBMITTER: Skorupka A 

PROVIDER: S-EPMC11666048 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature