Figure 4.

Graph theory principles. Graphs can represent any kind of network. Dots represent nodes, and lines connecting the dots are the connections. The degree (K) of a node is it's number of connections. The clustering coefficient (C), measuring local connectivity of a node, is the likelihood that its neighbors are connected. For node C, with neighbours B and D, the clustering coefficient is 1. The characteristic path length (L), a measure of global connectivity, is the minimum number of connections between two nodes. The path length between vertices A and B consists of three edges, indicted by the striped lines. The degree correlation (R), a measure of network clustering according to degree, is the ratio of the degrees of two neighboring nodes. Figure taken with permission from Stam and Reijneveld. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics. 2007c; 1: 3.

de Haan et al. BMC Neuroscience 2009 10:101   doi:10.1186/1471-2202-10-101
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