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.

翻译：利用图分析与图学习已被证明是探索加密经济学（如网络效应、去中心化、代币经济学和欺诈检测）的有效方法。然而，现有研究主要集中于主流加密货币，即比特币（BTC）和以太坊（ETH），忽视了超过1万个加密货币项目的广泛多样性。这种局限性可能导致结果偏差。本文旨在将研究范围扩展至整个加密货币图谱，考察各类代币的完整生命周期。此外，我们拟开创先进方法论，包括图迁移学习与创新的"图之图"概念。通过突破BTC和ETH的研究局限，我们期望深化对加密经济学的理解，并推动更复杂的基于图的技术发展。