  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.
