# Analyzing the Bitcoin Network: The First Four Years
## 6 Analysis and Results
### Network Analysis
#### 6.2.4 Visual Analysis of the Bitcoin Network  
In the final part of the network analysis, the tool Gephi is applied to conduct a visual analysis of the Bitcoin network. The subgraph that is used is based on the tagged businesses excluding the top three businesses (SatoshiDICE, Mt.Gox, and Deepbit). Furthermore, only a certain amount of businesses were chosen that play an important role according to their number of transactions or the transaction value due to limits in computation power. The first visualization (Figure 36) shows the degree centrality per node given by the size of the node. In addition, the label tags are colored and sized by the degree centrality (large and dark blue labels are associated to a high degree and vice versa for the small degree). The nodes are colored according to their category. Figure 35: Average Clustering Coefficient per Country. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Aggregates**} & \multicolumn{2}{l}{**Bitcoin Graph**} & \multicolumn{2}{l}{**Random Graph**} \\ \cline{2-5}  & **AVG Clustering** & **AVG Shortest Path** & **AVG Clustering** & **AVG Shortest Path** \\ \hline
**Country** & & & & \\ China & 0.130 & 5.15 & 0.00071 & 4.45 \\ Brasil & 0.053 & 4.74 & 0.00115 & 4.45 \\ Italy & 0.078 & 4.39 & 0.00091 & 4.23 \\ Argentina & 0.078 & 4.78 & 0.00190 & 4.25 \\ \hline
**Business** & & & & \\ Wallets & 0.085 & 3.90 & 0.00402 & 3.10 \\ Vendors & 0.048 & 3.32 & 0.062 & 1.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Investigating the Small World Phenomenon on Subgraphs. When looking at the nodes in the network, one can see that they form groups of clusters for certain businesses, especially in case of the gambling business (DICEonCRACK, Betcoins, and BitMillion.com). This confirms the high coefficient from the previous clustering analysis. The highest degree centrality in this subgraph can be related to the web wallet business (Instawallet and Strongcoin), the gambling businesses, and the donation business (Faucet Donation). Figure 37 shows the visualization of the clustering coefficients per node in the Bitcoin network. Interestingly, the nodes with a high degree centrality have a very small clustering coefficient, but the nodes around show rather high values, especially in case of the gambling businesses. The highest clustering coefficients can be seen for the exchange platform MPEx and for the nodes around the gambling businesses. In contrast, the values for the mining business (dark blue nodes) are among the lowest in the network. This again confirms the previous analysis on clustering in the Bitcoin economy, even on a much smaller scale. Figure 38 shows the aggregated transaction volume per node. Two nodes that are associated to the vendor Silkroad have the highest value in this particular subgraph. Other nodes that transact higher volumes of Bitcoins belong to the exchange business (Bitcoin-24.com, MPEx) and the wallet business (Instawallet). In contrast, the gambling, donation, and media/news business transact rather low values of Bitcoins in the network. This confirms previous descriptive statistics on the business aggregates. Figure 36: Visualization of Degree Centrality for Important Subgraph. Another interesting node attribute, the node size, is visualized in Figure 39. The node size is determined by the number of public keys that belong to a user or economic entity and can be seen as clusters of public keys. The vendor Silkroad incorporates nearly 210 thousand public keys, while the web wallet service Instawallet and the gambling business BitMillions.com have around 110 thousand associated public keys. Goods traded on Silkroad are often related to prohibited items such as weapons or drugs; hence, anonymity is essential for the participants. The execution of transactions via several thousand public keys and the usage of newly generated public keys for every new transaction might increase anonymity.