# Analyzing the Bitcoin Network: The First Four Years
## 7 Conclusion, Limitations and Outlook
Investigation of the degree distribution and power law over time reveals that the Bitcoin network follows a power law distribution over large parts of the value range. Since 2010, the Bitcoin network can be considered as a scale-free network with a power law slope coefficient \(\alpha\) in the range between 2.0 and 2.6 in the time horizon from 2010 to 2013. The degree distributions for particular businesses show strong heavy tails for the gambling, mining, and exchange business. These business categories are mainly driven by one business with an abnormal high degree. The power law slope coefficient \(\alpha\) for all business categories (except the wallets business) is in the range between two and three,indicating a scale-free network. On the country level, the degree distributions show a similar result. All considered countries have a power law slope coefficient \(\alpha\) between two and three, indicating the existence of a scale-free network. The analysis reveals that the majority of the investigated subgraphs of the Bitcoin network are scale-free networks. To identify major hubs in the Bitcoin network, the degree centrality was analyzed. The results on the entire Bitcoin network in the most active time from September 2012 to April 2013 reveal that the major hub nodes are controlled by the exchange platform Mt.Gox, the gambling service SatoshiDICE, and the web wallet service Instawallet. Next, the degree centrality was analyzed on the business aggregates. The results show that the dominant services in a business category are also the major hubs in the network. This is especially the case for SatoshiDICE in the gambling business, Mt.Gox in the exchange business, Deepbit in the mining business, and Instawallet in the wallet business category. The analysis of the average clustering coefficient indicates the existence of the small world phenomenon in the Bitcoin network over time as well as on the country and business aggregation level. This kind of analysis needs high computation power and was therefore tested on minor subgraphs on the business and country aggregation level. The existence of the small world phenomenon could be demonstrated for the country aggregations China, Brazil, Italy, and Argentina. For the business aggregations, the wallet and vendor businesses were investigated. Only the wallet business could be considered as small world network. The vendor business missed the requirements. This shows that a rather high clustering coefficient is just a first indicator and needs further investigation. Further network statistics could be applied on a representative subgraph of the Bitcoin economy to identify clusters, hubs, brokers, and most central nodes in the network. Furthermore, particular Bitcoin nodes and their interaction in the network could be identified and a geographic visualization of the subgraph was realized. This gives new insights on the Bitcoin economy in a visual way. Several interesting aspects of the Bitcoin economy could be covered in this work, but there are some limitations that could be addressed in future research. Extensions of this work should contain most recent data of the Bitcoin network to get insight on new developments in the economy, such as the attack on Mt.Gox with the subsequent closing of the exchange platform, or the closing of the vendor Silkroad. Furthermore, the intense fluctuations of the BTC/USD exchange rate in late 2013, resulting in a record high over $1,200 per Bitcoin, could be investigated. Another method would be time series analysis on economic distressed countries such as Spain, Cyprus, or Argentina and investigations on how the Bitcoin economy evolved during this time. One could also analyze the economic development in certain countries with appropriate economic measures and regress it against Bitcoin variables. Although events that explain the movements have been presented in this work, one could link these to network analysis and also visually investigate the Bitcoin transaction flows.