Working on the blockchain gang: Crunching and visualizing bitcoin data

Benedikt Koehler offers approaches to analyzing and visualizing bitcoin dataaccessing and downloading the blockchain, transforming the data into a networked data format, identifying hubs and clusters, and visualizing the results as dynamic network graphsso that typical patterns and anomalies can quickly be identified.

After social-media and smartphone data, cryptocurrency data will be the third wave of exciting new data sources for analyzing human behavior, in this case through economic transactions. Because every transaction refers to all previous transactions, the economics of bitcoin create a genuine networked structure. Furthermore, as had been the case with social-media data, one of the key features of bitcoin is that all its transactions are recorded in the blockchain and are publicly available. Benedikt Koehler offers approaches to analyzing and visualizing bitcoin data—accessing and downloading the blockchain, transforming the data into a networked data format, identifying hubs and clusters, and visualizing the results as dynamic network graphs—so that typical patterns, such as donations to different causes, and anomalies (e.g., hubs with very large numbers of transactions or abnormally large centrality or betweenness parameters) can quickly be identified.