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

TL;DR

These findings, rooted on extensive experimental results, 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.

Abstract

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.

Figures (8)

• Figure 1: List of attributes that identify an extrinsic as a successfully completed DOTs transfer between users, i.e., validated by the community and stored in the ledger.
• Figure 2: Distribution of the intra-exchange transactions. Slices are proportional to \ref{['fig:distribution_ec']} the number of transactions and \ref{['fig:distribution_ef']} the total transaction amount, as reported in Table \ref{['tab:exchanges']}.
• Figure 3: Exchanges ecosystem within the contracted graph represented in terms of \ref{['fig:graph_ec_EE']} number of transactions and \ref{['fig:graph_ef_EE']} total transaction amount. Nodes size is proportional to the intra-cluster transactions reported in Table \ref{['tab:exchanges']} while edges width is proportional to the related inter-exchange set of transactions.
• Figure 4: Distribution of the transactions within the network in terms of \ref{['fig:distribution_ec']} number and \ref{['fig:distribution_ef']} total amount.
• Figure 5: Distribution of the nodes in the original network $G$. Exchanges are segmented according to the number of nodes each exchange service is made of, as detailed in Table \ref{['tab:exchanges']}. Users are segmented depending on the size of the cluster they belong to, as detailed in Table \ref{['tab:nodes_distribution']}. Do note that users in 'size 1' are isolated w.r.t. other users.