Network Statistics
Network statistics allow for the descriptive characterization of a network graph and the identification of meaningful connections. In this study, we applied various network analysis approaches to the DDN to identify the most crucial disease nodes, as well as to automate the extraction of disease cluster subnetworks. We used the statistical packages available as plug-ins within Gephi to perform all of the network analytics.

Hub Diseases
Hub nodes are those that have significantly more edges than other nodes. These nodes are important because they play a critical role in the centrality of the network. There are a number of ways to measure centrality of a network and, hence, identify hub disease nodes. In this case, we used a measure called betweenness centrality to identify such nodes in the DDN. Betweenness centrality for a given node (ni) is calculated on the basis of the number of shortest paths between two other nodes (nj,nk) in the network and the number of times these paths pass through the node (ni). We computed the betweenness centrality for all pairs of nodes across the whole network. The mathematical notation of betweenness centrality is as follows:CB(ni)=∑j,kgj,k(ni)gj,kgj,kShortestpathlinkingnodejandkgj,k(ni)NumberofpathspassingthroughnodeiThe nodes with a high betweenness centrality value tend to be most important for keeping the network connected. We used this measure to change the representation of the nodes in the network by scaling the node size based on its betweenness centrality. In this way, we were able to visually identify the most important disease nodes in the network on the basis of network statistics.