ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System
Pages 1824 - 1834
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.
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References
[1]
Darcy WE Allen. 2023. Crypto Airdrops: An Evolutionary Approach. Available at SSRN 4456248 (2023).
[2]
Darcy WE Allen, Chris Berg, and Aaron M Lane. 2023. Why airdrop cryptocurrency tokens? Journal of Business Research, Vol. 163 (2023), 113945.
[3]
Blur. 2022. Wash traders were filtered out! Blur. https://twitter.com/blur_io/status/1600262957816254466 Retrieved Feb 6, 2024 from
[4]
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision. 9650--9660.
[5]
Hongzhou Chen, Haihan Duan, Maha Abdallah, Yufeng Zhu, Yonggang Wen, Abdulmotaleb El Saddik, and Wei Cai. 2023. Web3 Metaverse: State-of-the-art and vision. ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 20, 4 (2023), 1--42.
[6]
Davide Costa, Lucio La Cava, and Andrea Tagarelli. 2023. Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction. In Proceedings of the ACM Web Conference 2023. 1875--1885.
[7]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[8]
Andreas Diekmann and Ben Jann. 2010. Benford's law and fraud detection: Facts and legends. German economic review, Vol. 11, 3 (2010), 397--401.
[9]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
[10]
Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai. 2023. Altruistic and Profit-oriented: Making Sense of Roles in Web3 Community from Airdrop Perspective. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1--16.
[11]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855--864.
[12]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017).
[13]
Jiahao Hu, Mingpei Cao, Xizhe Zhang, Xiong Zhang, and Yuesheng Zhu. 2023 a. Temporal Weighted Heterogeneous Multigraph Embedding for Ethereum Phishing Scams Detection. In 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 1208--1213.
[14]
Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, and Ling Liu. 2023 b. BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection. In Proceedings of the ACM Web Conference 2023. 2189--2197.
[15]
Hiroki Kanezashi, Toyotaro Suzumura, Xin Liu, and Takahiro Hirofuchi. 2022. Ethereum Fraud Detection with Heterogeneous Graph Neural Networks. ArXiv, Vol. abs/2203.12363 (2022). https://api.semanticscholar.org/CorpusID:247619169
[16]
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, Vol. 30 (2017).
[17]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[18]
Dan Lin, Jiajing Wu, Qi Yuan, and Zibin Zheng. 2020a. Modeling and understanding ethereum transaction records via a complex network approach. IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 67, 11 (2020), 2737--2741.
[19]
Dan Lin, Jiajing Wu, Qi Yuan, and Zibin Zheng. 2020b. T-edge: Temporal weighted multidigraph embedding for ethereum transaction network analysis. Frontiers in Physics, Vol. 8 (2020), 204.
[20]
Derek Liu, Francesco Piccoli, Katie Chen, Adrina Tang, and Victor Fang. 2023. NFT Wash Trading Detection. arXiv preprint arXiv:2305.01543 (2023).
[21]
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).
[22]
Zheng Liu and Hongyang Zhu. 2022. Fighting Sybils in Airdrops. arXiv preprint arXiv:2209.04603 (2022).
[23]
Wai Weng Lo, Gayan K Kulatilleke, Mohanad Sarhan, Siamak Layeghy, and Marius Portmann. 2023. Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin. Applied Intelligence (2023), 1--12.
[24]
Christos A Makridis, Michael Fröwis, Kiran Sridhar, and Rainer Böhme. 2023. The rise of decentralized cryptocurrency exchanges: Evaluating the role of airdrops and governance tokens. Journal of Corporate Finance, Vol. 79 (2023), 102358.
[25]
David Marsanic. 2023. Blur Airdrop: Just 23 Users Received More Than $1 Million in BLUR Each. https://dailycoin.com/blur-airdrop-23-users-got-more-than-1-million-in-blur/ Retrieved Oct 4, 2023 from
[26]
Matthieu Nadini, Laura Alessandretti, Flavio Di Giacinto, Mauro Martino, Luca Maria Aiello, and Andrea Baronchelli. 2021. Mapping the NFT revolution: market trends, trade networks, and visual features. Scientific Reports, Vol. 11, 1 (Oct. 2021), 20902. https://doi.org/10.1038/s41598-021-00053--8 Number: 1 Publisher: Nature Publishing Group.
[27]
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701--710.
[28]
NFTGo Research. 2023. NFT Market Report -- Q1 2023. NFTgo. https://nftgo.io/research/nft-insights/nft-market-report-q1--2023-en/ Retrieved Sep 4, 2023 from
[29]
Jie Shen, Jiajun Zhou, Yunyi Xie, Shanqing Yu, and Qi Xuan. 2021. Identity inference on blockchain using graph neural network. In Blockchain and Trustworthy Systems: Third International Conference, BlockSys 2021, Guangzhou, China, August 5--6, 2021, Revised Selected Papers 3. Springer, 3--17.
[30]
Dan Sheridan, James Harris, Frank Wear, Jerry Cowell Jr, Easton Wong, and Abbas Yazdinejad. 2022. Web3 challenges and opportunities for the market. arXiv preprint arXiv:2209.02446 (2022).
[31]
Vladimir Vapnik. 1999. The nature of statistical learning theory. Springer science & business media.
[32]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. 2017. Graph attention networks. stat, Vol. 1050, 20 (2017), 10--48550.
[33]
Jiajing Wu, Qi Yuan, Dan Lin, Wei You, Weili Chen, Chuan Chen, and Zibin Zheng. 2020. Who are the phishers? phishing scam detection on ethereum via network embedding. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 52, 2 (2020), 1156--1166.
[34]
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?arxiv: 1810.00826 [cs.LG]
[35]
Dunjie Zhang, Jinyin Chen, and Xiaosong Lu. 2021. Blockchain phishing scam detection via multi-channel graph classification. In Blockchain and Trustworthy Systems: Third International Conference, BlockSys 2021, Guangzhou, China, August 5--6, 2021, Revised Selected Papers 3. Springer, 241--256.
[36]
Joel Zhao. 2023. What BLUR Did To Surpass Opensea. https://chaindebrief.com/what-blur-did-to-surpass-opensea Retrieved Oct 4, 2023 from
[37]
Jiajun Zhou, Chenkai Hu, Jianlei Chi, Jiajing Wu, Meng Shen, and Qi Xuan. 2022. Behavior-aware account de-anonymization on ethereum interaction graph. IEEE Transactions on Information Forensics and Security, Vol. 17 (2022), 3433--3448.
Index Terms
- ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System
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Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
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Published: 13 May 2024
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