### Large number of U2U transactions

#### ego networks

We start our analysis by measuring the extent of the U2U network around each DWM. The percentages of users forming U2U pairs vary across DWMs, with a median value of 38% (min 23%, max 68%). The variance of the percentage of users with U2U pairs is shown in Fig. 2a. The figure shows that the number of users with U2U pairs is nearly monomial in the number of users interacting with a DWM, with an estimated exponent equal to 1.06 and (R^2 = 0.969) , see Section S1 for details on the mounting procedure. The total volume of transactions that users send to the marketplace is essentially equivalent to the volume that they receive from it (two-sided Wilcoxon test33: (L=330), (p=0.282)). It is important to note that the total volume of transactions that users send to a DWM (and therefore that they receive from it) is always less than the volume exchanged via U2U transactions, as shown in Figure 2b.

Similar results hold for the full network, confirming that U2U pair formation is a ubiquitous phenomenon around DWMs. The total volume of transactions that users sent to DWMs was $3.8 billion, the volume received from DWMs was$3.7 billion, while the volume traded via U2U pairs reached $30 billion. In Figure S3, we illustrate the number of transactions, the volume of transactions and the lifetime of U2U pairs. In all cases, we observe familiar fat-tailed distributions. We then consider the temporal evolution of transactions. We look at trade volume over time in Figure 2c, which shows that since 2011, U2U trades have always involved monthly volume greater than the volume sent to all DWMs. This highlights the economic importance of U2U transactions in the Bitcoin ecosystem compared to DWMs. ### U2U network behavior Now we will analyze users focusing on the following groups: users who do not form stable U2U pairs; users who form stable U2U pairs, of which there are users who met outside DWM and users who met inside DWM (see nomenclature in Table 2). We start by focusing our attention on identifying stable U2U pairs, that is, persistent pairs in the U2U network. For this purpose, we use the scalable activity-driven model31 to identify stable pairs in a statistically sound way (see Methods). We see that 137,667 stable U2U pairs were formed by 106,648 users and generated a trading volume of$1.5 billion. Stable pairs produce five times more trades per pair than non-stable pairs (Mann-Whitney-U two-tailed test34: MNU (=45.8 cdot 10^9), (p) corresponding to a volume of transactions 5.34 times higher (MNU (= 317 cdot 10^9), (p), see Figure S4. Stable pairs, although they represent less than 2% of the total number of U2U pairs, generate a disproportionate volume of transactions.

The high activity of users forming stable U2U pairs is not limited to the U2U network – these users are also the most active in trading with DWMs. Users of U2U stable pairs spend a median of 41 days on DWMs compared to a median of only one day for users without stable pairs. The two resulting distributions are significantly different (two-tailed Kolmogorov-Smirnov test35:KS (= 0.673), (p), see the box in Fig. 3. When we look at trading volume with DWMs, we find qualitatively similar results. Users of U2U stablecoins transact median $400 with DWMs, while other users only transact for$56. The two resulting distributions are significantly different (KS (= 0.438), (p), see Fig. 3. These results are valid not only for the complete network, but for each DWM in our data, see Figures S5 and S6.

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