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www.nature.com/scientificreports/ Scientific Reports | Vol:.(1234567890) Figure 3. Secondary sale prices. Sales over time for the top collection in terms of number of sales in each NFT category (CryptoKitties, Stf.capcorn, Alien, Decentraland, Miscellanea, and Unstoppable). Each horizontal line represents an NFT and each dot a sale. Sales are coloured based on the change in price compared to previous sale (see colourbar). implying the distribution of sizes is broad. We find that ∼ 75% of collections comprise less than 37 unique assets, and ∼ 1% have more than 10,400 unique assets. Temporal patterns of secondary sales are unique for each collection, as evidenced by considering the top col- lection in each category (see Fig. 3). For example, when Cryptokitties emerged in 2017, secondary sale prices were typically lower than the price of their first sale. More recently in 2021, instead, their secondary sale prices have gone up because of an increase in the number of potential customers. Other collections, like Alien, alternated periods when secondary sale prices went down and period when they went up. In the Unstoppable collection secondary sales are rare because NFTs correspond to web domains secured by blockchain technology. In 2017, secondary sale price were lower than primary in 66% of the cases, while in 2021 only 27% of secondary sales had lower prices than the primary one. The networks of NFT trades. How do traders interact with each other? Are there central actors? We approach these questions adopting a network science approach23,27. We consider the network of trades, where nodes are traders, a directed link from a trader to another exists if the former (the buyer) purchases at least one NFT from the latter (the seller). Each link has a weight corresponding to the total number of items that the buyer bought from the seller. First, we study the behaviour of individual NFT traders by focusing on properties of the nodes. We find that traders activity is highly heterogeneous: the strength of traders (nodes) s, defined as the total number of purchases and sales made by each trader, is distributed as a power law P(s) ∼ s1 with exponent 1 = −1.85 (see Fig. 4a), such that the top 10% of traders alone perform 85% of all transactions and trade at least once 97% of all assets. Further, we find a superlinear relation between the strength of a trader and the total number of days of activity d, with s ∼ d2 and 2 = 1.28 (see Fig. 4b). This result reveals that the average number of daily trades is larger for traders active over long periods of time. Traders are also specialized: measuring how individuals distribute their trades across collections, we find that traders perform at least 73% of their transactions in their top collection, while at least 82% in their top two collections combined. The relation between strength and specialization is not monotonic: the most specialized traders have either few (less than ten) or many (more than ten thousands) transactions (see Fig. 4c). A specialized trader is the one with Ethereum address “0xfc624f8f58db41bdb95ae- dee1de3c1cf047105f1”, that exchanges tens of thousands of CryptoKitties. Similar relationships hold when buying and selling behaviours are considered separately (see “SI”). Secondly, we turn to properties of the network links, describing interactions between pairs of traders. We find that the distribution of link weights is well characterized by a power law distribution, with the top 10% of buyer–seller pairs contributing to the total number of transactions as much as the remaining 90% (see “SI”). An interesting question is whether traders connect preferentially to traders that have similar strength. We tackle this question by studying the assortativity coefficient r28, that measures the correlation between the sum of the weights of all outgoing links (the outgoing strength) of a given node with the average sum of the weights of incoming links (the incoming strength) of its neighbours. We find that the assortativity, which takes value r = −0.024, (2021) 11:20902 | https://doi.org/10.1038/s41598-021-00053-8 4PDF Image | Mapping the NFT revolution
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