Temporal Relational Ranking for Non-Fungible Tokens

Abstract

NFTs (Non-Fungible Tokens) are a type of digital asset based on blockchain technology that has become an attractive investment tool for many investors. This paper proposes a model based on knowledge graphs and LSTM (Long Short-Term Memory) networks to provide investors with a ranking prediction of future NFT returns, assisting traders in making more accurate investment decisions in the NFT market. To verify the usability and accuracy of the model, we collected data from 345 different types of NFTs on the OpenSea platform and conducted experimental validation, comparing our method with other benchmark methods to demonstrate the accuracy of our proposed method in practical scenarios.