Figure 5
Co-bidding networks. (A) Schematic description of various co-bidding networks based on NFT transactions extracted from the data, capturing how a collector \(C_1\) bids on an NFT \(N_1\) created by artist \(A_1\). As collectors can bid on multiple artworks, we use these joint bids to reconstruct the collector–collector, NFT–NFT and artist–artist networks. (B) The collector network has collectors as nodes and their joint NFT bids as links. The nodes are sized based on maximum investment and the top 10 collectors are highlighted while other collectors are colored in pink. (C) The distribution of edge weights, representing competition, follows a power law decay \(P(w_{collector}) \propto w_{collector}^{-\beta _{collector}}\) with exponent \(\beta _{collector} = 3.7\), measured using plfit31. (D) The association of maximum bidding amount and degree, finding that highly connected collectors make higher bids. (E) The NFT network, where a node is an NFT, connected if the same collector has bid on both the NFTs. The node sizes correspond to the selling price and the NFTs bid by the top 10 collectors are highlighted. (F) The distribution of link weights, representing collector similarity, follows a fat tail decay with exponent \(\beta _{NFT} = 3.4\). (G) The association of connectivity and selling price, finding that central NFTs attract higher prices. (H) The artist network, whose nodes are artists and links correspond to joint collectors. The nodes sizes reflect the total earnings of the artist. We color the artists based on bids by top 10 collectors while the rest are colored pink, highlighting the stratification of artists among collectors. (I) The distribution of edge weights, representing similar collector interest, follows a fat tail decay with exponent \(\beta _{artist} = 3.9\).
