WWW '24: Proceedings of the ACM on Web Conference May 2024, pp. 1824 - 1834 .
ISSN/ISBN: Not available at this time. DOI: 10.1145/3589334.3645597
Abstract: As Web3 projects leverage airdrops to incentivize participation, airdrop hunters tactically amass wallet addresses to capitalize on token giveaways. This poses challenges to the decentralization goal. Current detection approaches tailored for cryptocurrencies overlook non-fungible tokens (NFTs) nuances. We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. ARTEMIS captures NFT airdrop hunters through: (1) a multimodal module extracting visual and textual insights from NFT metadata using Transformer models; (2) a tailored node aggregation function chaining NFT transaction sequences, retaining behavioral insights; (3) engineered features based on market manipulation theories detecting anomalous trading. Evaluated on decentralized exchange Blur's data, ARTEMIS significantly outperforms baselines in pinpointing hunters. This pioneering computational solution for an emergent Web3 phenomenon has broad applicability for blockchain anomaly detection. The data and code for the paper are accessible at the following link: hrefhttps://doi.org/10.5281/zenodo.10676801 doi.org/10.5281/zenodo.10676801.
Bibtex:
@inproceedings{,
author = {Zhou, Chenyu and Chen, Hongzhou and Wu, Hao and Zhang, Junyu and Cai, Wei},
title = {ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System},
year = {2024},
isbn = {9798400701719},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589334.3645597},
doi = {10.1145/3589334.3645597},
booktitle = {Proceedings of the ACM on Web Conference 2024},
pages = {1824–1834},
numpages = {11},
keywords = {airdrop hunters, graph neural network, multimodal deep learning, nfts, web3},
location = {Singapore, Singapore},
series = {WWW '24}
}
Reference Type: Conference Paper
Subject Area(s): Computer Science