Non-Markovian paths and cycles in NFT trade
Published in ComplexDataBlocks, 2022
Recommended citation: H. Yousaf, N. Arnold, R. Lambiotte, T. Larock, R. Clegg, P. Zhong, A. Alnaimi, B. Steer. (2022). "Non-Markovian paths and cycles in NFT trades. " ComplexDataBlocks: Data Science and Complexity on the Blockchain. Conference on Complex Systems Workshop (2022). 1(1). http://academicpages.github.io/files/paper1.pdf
In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. NFTs are digital assets such as art or memberships that are traded online between agents, often via smart contracts on a blockchain. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.
Recommended citation: H. Yousaf, N. Arnold, R. Lambiotte, T. Larock, R. Clegg, P. Zhong, A. Alnaimi, B. Steer, “Non-Markovian paths and cycles in NFT trades”, ComplexDataBlocks: Data Science and Complexity on the Blockchain. Conference on Complex Systems Workshop (2022).