ChainNet: Learning on Blockchain Graphs with Topological Features

TL;DR

This paper introduces ChainNet, a novel approach using topological features derived from persistent homology to improve Bitcoin price prediction by capturing complex network dynamics in blockchain graphs.

Contribution

It presents a new graph representation learning method leveraging topological features, outperforming traditional metrics in predicting cryptocurrency price fluctuations.

Findings

Topological features significantly improve Bitcoin price prediction accuracy.

Persistent homology captures higher-order network interactions.

ChainNet offers a computationally efficient and extendable framework.

Abstract

With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether the dynamics of the transaction graph impacts the price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. %explain higher order interactions among the nodes in Blockchain graphs and can be used to build much more accurate price prediction models. Using the proposed persistent homology-based techniques, we offer a new elegant, easily extendable and computationally light approach for graph representation learning on Blockchain.