When Crypto Economics Meet Graph Analytics and Learning
<|reference_start|>When Crypto Economics Meet Graph Analytics and Learning: Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency projects. This oversight may result in skewed insights. In our paper, we aim to broaden the scope of investigation to encompass the entire spectrum of cryptocurrencies, examining various coins across their entire life cycles. Furthermore, we intend to pioneer advanced methodologies, including graph transfer learning and the innovative concept of "graph of graphs". By extending our research beyond the confines of BTC and ETH, our goal is to enhance the depth of our understanding of crypto economics and to advance the development of more intricate graph-based techniques.<|reference_end|>
@article{luo2024when,
    title={When Crypto Economics Meet Graph Analytics and Learning},
    author={Bingqiao Luo},
    journal={arXiv preprint arXiv:2403.06454},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06454},
    primaryClass={cs.CE}
}