Analyzing Transaction Graphs via Motif-Based Graph Representation Learning for Cryptocurrency Price Prediction

Abstract Decentralized and transparent nature of cryptocurrencies have lately increased investors interest in them. Forecasting cryptocurrency’s price accurately is crucial to come up with a good investment strategy, and such a forecast requires one to consider its unique attributes as well as high volatility. Even though many existing studies have focused on analyzing the cryptocurrency transaction graph topology, studies on the analysis of transaction graph’s impact on prices are quite limited. In this paper, we explore the forecasting ability of blockchain transaction graph-based attributes on Bitcoin’s and Ethereum’s future price via deep learning methods. More specifically, we came up with motif convolution module (MCM), a motif-based graph representation learning approach to take local structural knowledge into account more strongly in node and edge-attributed transaction graphs encoding substantial structural knowledge. Our proposed MCM constructs a motif dictionary without supervision, and employs a new motif convolution operation while extracting the vertices local structural context. Afterwards, we learn high-level vertex embeddings by using such structural context via multilayer perceptron and graph neural network. Overall, we extract the attributed transaction graphs temporally-evolving low-dimensional representations, and use such embedding data together with historical prices within self-attention-based LSTM to predict the future prices accurately. Our proposed approach outperforms all considered baselines in terms of both price and price direction prediction, showing the promise of efficient integration of transaction data into cryptocurrency price prediction.