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www.nature.com/scientificreports/ Scientific Reports | Vol:.(1234567890) Figure 5. Networks visualization. (a) Trader network, where nodes represent traders and links sales between a pair of them. (b) NFT network, where nodes represent NFTs and links when a pair of NFTs is purchased in “sequence”. For visualization purposes, we selected the ten top collections in the Art category on February 2021. Visualization is done using Netwulf31. NFT network shows a similar trend, where NFTs, albeit surrounded by other NFTs in the same collection, tend to form a sparser structure (see Fig. 5b). We then study the networks consisting of assets in the same category and blockchain (see “SI”). We find that key results presented above, including the shape of the strength distributions, hold across categories. Also in this case, we find that traders, independently from the category considered, are specialized: the fraction of individual trades in the top collection is included between 59%, for the Other category, and 98%, for the Utility category. Similarly, the fraction of individual trades in the top collection is 70% for the WAX blockchain and 91% for the Ethereum blockchain category. Relative to the number of total NFTs in each category, the WAX component con- tains 55.0% of all NFTs labeled as Collectible, but only the 0.06% of all NFTs labeled as Utility. On the contrary, the Ethereum component has the 54.8% of all Art, but only the 10.6% of Games. Visual features. NFTs are linked to digital assets of different types, including videos, text, animated GIFs, and audio. Currently, the most popular NFTs are images10,11. We select NFTs associated with images and take a snapshot of animated GIFs, and analyse them with the pre-trained convolution neural network AlexNet. AlexNet extracts from an image a vector of 4096 values that is a dense representation of the image’s visual features. With this representation, vectors extracted from images that are visually similar are close in the vector space. To quantify the visual difference between pairs of pictures, we calculated the cosine distance (CD) between them, a value that goes from zero (for identical images) to one (for highly different images). We measured such distance between pictures within the same collection and across collections. The average CD calculated between items which belong to the same collection is significantly lower (μ = 0.59, σ = 0.20) compared to the one obtained for objects from two different collections (μ = 0.87, σ = 0.06), con- firming an intra-collection graphical homogeneity. Figure 6a shows the matrix of average CD values between all pairs of collections. Values on the diagonal represent the intra-collection CD values, and reveal that most col- lections have a high degree of homogeneity (e.g., Sorare (CD = 0.24) or Cryptopunks (CD = 0.33)) but some are more heterogeneous (Rarible (CD = 0.89)). In short, many collections have their own style, graphical hallmarks that distinguish them from others. There are also sub-groups of collections, usually within the same category (coloured band in Fig. 6a), which share some common visual features. This is the case for collections containing pieces of pixel-art, including Chubbie, Cryptopunks and Wrapped Punks, or the similarities observed between Cryptokitties and Axie. To map the images into a lower-dimensional feature space that can be used in practice for prediction and visu- alization, we apply Principal Component Analysis (PCA) to the AlexNet vectors. PCA uses linear combinations of the 4096-dimensional vectors to project them into vectors with an arbitrarily lower number of dimensions and such that the variance of datapoints in the projected space is maximized. Considering the whole sample, (2021) 11:20902 | https://doi.org/10.1038/s41598-021-00053-8 6PDF Image | Mapping the NFT revolution
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