  The growth potential of a crypto(currency) project can be measured by the use
cases spurred by the underlying technology. However, these projects are usually
distributed, with a weak feedback schemes. Hence, a metric that is widely used
as a proxy for their healthiness 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 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 clusters interactions.
  To show the viability of our solution, we bring forward a use case centered
on Polkadot, which 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 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 (> 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, while characterized by a high level of
scalability and adaptability, are at the same time immune from the drawbacks of
currently used metrics.
