Monero
Monero's (XMR) transaction volume is impossible to calculate due to the design of the blockchain, which hides transaction amounts \cite{analytics}. However, we can model using metrics of economic activity (e.g. paymentCount), economic agent adoption (e.g. generatedCoins), end-user adoption (e.g. activeAddresses), among others . To study the XMR peripheral network evolution we map the network structure changes (across time) for websites that were frequently visited by the same visitors of the top services in the Monero economy (wallets, etc). We use anomaly detection to separate the persistent services from those that join (or leave) the economy more frequently. We use this insight to profile the novice Monero user persona, and to identify emerging sources of risk. Additionally, we map the geographic distribution and relative popularity of those services, across different regions and time scales.
The exploration of 500 timeseries, as shown in Figure 8, shows what services maintained high usage levels across the 18 months period, which ones started strong and lose steam, which ones started small and gradually gained a popularity, and which ones did not survive. The high density region in December 2017 corresponds to a crypto market rally.