  A main focus in economics research is understanding the time series of prices
of goods and assets. While statistical models using only the properties of the
time series itself have been successful in many aspects, we expect to gain a
better understanding of the phenomena involved if we can model the underlying
system of interacting agents. In this article, we consider the history of
Bitcoin, a novel digital currency system, for which the complete list of
transactions is available for analysis. Using this dataset, we reconstruct the
transaction network between users and analyze changes in the structure of the
subgraph induced by the most active users. Our approach is based on the
unsupervised identification of important features of the time variation of the
network. Applying the widely used method of Principal Component Analysis to the
matrix constructed from snapshots of the network at different times, we are
able to show how structural changes in the network accompany significant
changes in the exchange price of bitcoins.
