Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump
Summary: SNN encodes a Telegram channel's P&D history with positional attention to forecast target-coin pump probability before scheduled time. Study of 709 P&D events reveals intra-channel homogeneity and inter-channel heterogeneity; code and data on GitHub. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Sihao Hu (National University of Singapore)
- 2. Zhen Zhang (National University of Singapore)
- 3. Shengliang Lu (National University of Singapore)
- 4. Bingsheng He (National University of Singapore)
- 5. Zhao Li (Link2Do Technology; Zhejiang University)
BibTeX Citation
@inproceedings{hu_sigmod23,
title = {{Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump}},
author = {Hu, Sihao and Zhang, Zhen and Lu, Shengliang and He, Bingsheng and Li, Zhao},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588686},
url = {https://dl.acm.org/doi/10.1145/3588686},
year = {2023}
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 4,510 | Building High Throughput Permissioned Blockchain Fabrics: Challenges and Opportunities | 2020 | VLDB | 6.6520543e-05 |
| 7,011 | GPU-Accelerated Graph Label Propagation for Real-Time Fraud Detection | 2021 | SIGMOD | 5.7263348e-05 |
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