One method to measure node centrality is simply to sum all edges connected to a node. This is known as the “degree centrality”, or simply the degree, of a node (Figure 3). High-degree nodes are called hubs, and can play important roles in network structure and dynamics. The second method is to calculate the fraction of all shortest paths in a network that cross over a given node (or edge). This proportion is the “betweenness centrality” of a node (or edge), which is a useful measure of how much information might traverse certain parts of a network, presuming that optimal paths are used. High values can identify nodes that are crucial bridges between communities and/or possible bottlenecks in network traffic, and may also identify hubs. The measure must be used with caution, however, since it may also yield high values at the periphery of networks if peripheral nodes have few possible paths into the main body of the graph (see Figure 3, node 2). These measures of centrality have been used for many purposes, one of which has been to identify hubs within MRI networks (Buckner et al., 2009; Hagmann et al., 2008).
