Characterizing Polkadot’s Transactions Ecosystem: methodology, tools, and insights

The growth potential of a crypto project, typically sustained by an associated cryptocurrency, can be measured by the use cases spurred by the underlying technology. However, these projects are implemented through decentralized applications, with a weak (if any) feedback scheme. Hence, a metric that is widely used as a proxy for the healthiness of such projects is the number of transactions and related volumes. Nevertheless, such a metric can be subject to manipulation—the crypto market being an unregulated one, magnifies such a risk. To address the cited gap, in this paper, we design a comprehensive methodology to process large cryptocurrency transaction graphs that, after clustering user addresses of interest, derives a compact representation of the network that highlights interactions among clusters. The analysis of these interactions provides insights into/over/on the strength of the project.To show the quality and viability of our solution, we bring forward a use case centered on Polkadot. The Polkadot network, a cutting-edge cryptocurrency platform, has gained significant attention in the digital currency landscape due to its pioneering approach to interoperability and scalability. However, little is known about how many and to what extent its wide range of enabled use cases have been adopted by end-users so far. The answer to this type of question means mapping Polkadot (or any analyzed crypto project) on a palette that ranges from a thriving ecosystem to a speculative coin without compelling use cases.Our findings, rooted on extensive experimental results—we have parsed 12.5+ million blocks—, demonstrate that crypto exchanges exert considerable influence on the Polkadot network, owning nearly 40% of all addresses in the ledger and absorbing at least 80% of all transactions. In addition, the high volume of inter-exchange transactions (more than 20%) underscores the strong interconnections among just a couple of prominent exchanges, prompting further investigations into the behavior of these actors to uncover potential unethical activities, such as wash trading.These results are a testament to the quality and viability of the proposed solution that, while characterized by a high level of scalability and adaptability, is at the same time immune from the drawbacks of currently used metrics.