information transfer. However, local clustering (and thereby local efficiency) is also low, with the result that the potential for modular information processing is limited. In between these extremes are networks with predominantly locally structured connections, but also with a few random long-range connections (Figure 1A – center). In such graphs, known as small-world networks, the theoretical advantages of high clustering (local efficiency) that characterize regular networks are combined with the short average path-lengths (global efficiency) characteristic of random networks. Such small-world networks have high complexity, in that they are simultaneously functionally segregated (small subsets of the system can behave independently) and also functionally integrated (large subsets tend to behave coherently). (Sporns et al., 2000).
