Graph-Centric Crypto Price Prediction

In this paper we propose, for the first time in the literature, a graph-centric approach for crypto price prediction through the use of Graph Neural Networks, which can naturally be applied over transaction graphs and thus exploit the connectivity and structural features of Blockchains' transaction network. We apply our approach to the prediction of the most popular cryptocurrency currently on the market, Bitcoin. We demonstrate the effectiveness of our methods for short and longer term predictions considering several variants of the Bitcoin transaction graphs, additional external information and various basic architectures, achieving results with mean absolute percentage error as low as 1.069 %. Compared to the state-of-the-art, our method exhibits more useful results, in terms of both accuracy and predictive power.