2,380 research outputs found
Blockchain: A Graph Primer
Bitcoin and its underlying technology Blockchain have become popular in
recent years. Designed to facilitate a secure distributed platform without
central authorities, Blockchain is heralded as a paradigm that will be as
powerful as Big Data, Cloud Computing and Machine learning. Blockchain
incorporates novel ideas from various fields such as public key encryption and
distributed systems. As such, a reader often comes across resources that
explain the Blockchain technology from a certain perspective only, leaving the
reader with more questions than before. We will offer a holistic view on
Blockchain. Starting with a brief history, we will give the building blocks of
Blockchain, and explain their interactions. As graph mining has become a major
part its analysis, we will elaborate on graph theoretical aspects of the
Blockchain technology. We also devote a section to the future of Blockchain and
explain how extensions like Smart Contracts and De-centralized Autonomous
Organizations will function. Without assuming any reader expertise, our aim is
to provide a concise but complete description of the Blockchain technology.Comment: 16 pages, 8 figure
Network-based indicators of Bitcoin bubbles
The functioning of the cryptocurrency Bitcoin relies on the open availability
of the entire history of its transactions. This makes it a particularly
interesting socio-economic system to analyse from the point of view of network
science. Here we analyse the evolution of the network of Bitcoin transactions
between users. We achieve this by using the complete transaction history from
December 5th 2011 to December 23rd 2013. This period includes three bubbles
experienced by the Bitcoin price. In particular, we focus on the global and
local structural properties of the user network and their variation in relation
to the different period of price surge and decline. By analysing the temporal
variation of the heterogeneity of the connectivity patterns we gain insights on
the different mechanisms that take place during bubbles, and find that hubs
(i.e., the most connected nodes) had a fundamental role in triggering the burst
of the second bubble. Finally, we examine the local topological structures of
interactions between users, we discover that the relative frequency of triadic
interactions experiences a strong change before, during and after a bubble, and
suggest that the importance of the hubs grows during the bubble. These results
provide further evidence that the behaviour of the hubs during bubbles
significantly increases the systemic risk of the Bitcoin network, and discuss
the implications on public policy interventions
A Bayesian Approach to Identify Bitcoin Users
Bitcoin is a digital currency and electronic payment system operating over a
peer-to-peer network on the Internet. One of its most important properties is
the high level of anonymity it provides for its users. The users are identified
by their Bitcoin addresses, which are random strings in the public records of
transactions, the blockchain. When a user initiates a Bitcoin-transaction, his
Bitcoin client program relays messages to other clients through the Bitcoin
network. Monitoring the propagation of these messages and analyzing them
carefully reveal hidden relations. In this paper, we develop a mathematical
model using a probabilistic approach to link Bitcoin addresses and transactions
to the originator IP address. To utilize our model, we carried out experiments
by installing more than a hundred modified Bitcoin clients distributed in the
network to observe as many messages as possible. During a two month observation
period we were able to identify several thousand Bitcoin clients and bind their
transactions to geographical locations
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