Airdrop Hunter Detection via PageRank-Augmented Multimodal Graph Neural Networks

Abstract

Airdrops are a widely used mechanism in Web3 ecosystems to incentivize early users by distributing governance tokens. However, these mechanisms are increasingly targeted by airdrop hunters—malicious actors who exploit token distribution systems through address farming, automated scripts, and behavioral camouflage. While prior work such as ARTEMIS leverages multimodal features and local transaction patterns to detect such behavior, it lacks a global understanding of wallet influence in the transaction graph. In this paper, we propose an enhanced detection framework that augments the ARTEMIS by incorporating PageRank-based global centrality as an additional structural feature. This allows the model to better distinguish superficially active wallets from those with broader influence in the network. We evaluate our method on real-world Non-Fungible Token (NFT) data from the Blur marketplace and achieve state-of-the-art performance. Furthermore, a feature substitution experiment reveals that simple degree-based features alone can achieve near-perfect performance, even outperforming PageRank, suggesting that the labels are strongly coupled with topological properties. These findings highlight both the effectiveness of structural augmentation and the potential risks of shortcut learning in graph-based detection systems.