Using persistent homology based ideas, we offer an elegant, easily extendable and computationally light approach for graph representation learning on Blockchain networks to predict cryptocurrency prices.
We identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.
We introduce a novel concept of chainlets, or Bitcoin subgraphs, which allows us to evaluate the local topological structure of the Bitcoin graph over time
Aug 2017 Version 1.1
We offer a holistic view on Blockchain. Starting with a brief history, we give the building blocks of Blockchain, and explain their interactions. As graph mining has become a major part its analysis, we elaborate on graph theoretical aspects of the Blockchain technology
Bitcoin Graph Dataset: We provide annual and monthly data on the Bitcoin graph.