The rise of cryptocurrencies is one of the phenomena characterizing the past decade. What sets cryptocurrencies apart from the traditional ones is that no central party is required for enforcing the transaction rules and the transactions are also publicly available. Meanwhile, network analysis tools have become widely popular for explaining the complex world shaped by social interaction. Even though the Complex Networks approach has been used for inspecting Bitcoin, the most widely adapted cryptocurrency, no prior study investigates the dynamics of investor communities in Bitcoin networks. The existing studies mostly focus on directed Bitcoin transfer networks while behavioural synchronization networks have not been sufficiently addressed.
This thesis sheds a light on the social aspect of Bitcoin by exploring the dynamics of clusters of investors who time their trades similarly. To conduct such a research, we retrieve the public ledger of Bitcoin transactions and extract over 170 million Bitcoin wallets from the anonymous data. A network of active wallets is formed for each month from 2009 until the end of 2019, and two wallets are connected if their trade timing passes a statistical similarity test. Network analysis tools are used for detecting communities in the formed networks, and community evolution analysis is performed by analyzing the community structure of subsequent monthly networks.
Our results show that Bitcoin investor communities are mostly short-lived but some persist for months or even years. We also find out that the long-lived investor communities prefer splitting over merging when it comes to persistence methods. This research not only produces novel information, which is valuable as such, but also lays a solid basis for future studies concerned with the evolution of Bitcoin communities by bringing together best practices of varying disciplines
