Predicting User Performance and Bitcoin Price Using Block Chain Transaction Network

TL;DR

This paper analyzes Bitcoin transaction networks to predict user performance and Bitcoin prices by examining address reuse, user clustering, node roles, and network performance.

Contribution

It introduces a method to cluster Bitcoin addresses into users and categorizes nodes by their roles, linking network features to performance and price prediction.

Findings

Address reuse enables user clustering.

Node roles influence network dynamics.

Network features correlate with Bitcoin price movements.

Abstract

This work is organized as follows. In the first section we review the prior work and we have obtained our data. Next, we will look at address reuse in the Bitcoin network. We show that a great portion of users reuse their addresses which could enable us to cluster the addresses and attribute them to single users. Next, we will categorize the nodes based on their role in the network as a customer or seller. Finally, we do a study of nodes and network performance.