FRAUDAR

TLDR

Fraud detection on bipartite user–product or follower–followee graphs typically relies on identifying dense subgraphs, but fraudsters evade these methods by camouflaging with honest interactions or hijacking accounts. This study aims to identify fraudsters even when they camouflage or hijack accounts. We introduce FRAUDAR, an algorithm that resists camouflage, estimates upper bounds on fraudster effectiveness, and demonstrates strong performance on real data. Experiments show FRAUDAR outperforms the leading competitor in detecting both camouflaged and non‑camouflaged fraud, and in a 1.47‑billion‑edge Twitter follower graph it uncovered over 4,000 accounts linked to follower‑buying services.

Abstract

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look "normal". Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic. Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage-resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower-followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4000 detected accounts, of which a majority had tweets showing that they used follower-buying services.