  Recent years have witnessed the availability of richer and richer datasets in
a variety of domains, where signals often have a multi-modal nature, blending
temporal, relational and semantic information. Within this context, several
works have shown that standard network models are sometimes not sufficient to
properly capture the complexity of real-world interacting systems. For this
reason, different attempts have been made to enrich the network language,
leading to the emerging field of higher-order networks. 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. 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.
