Mathematically, networks can be represented as graphs, i.e. a group of interacting entities (nodes), connected by lines (edges), indicating which pairs of nodes directly interact. For our purposes these nodes can represent neurons, populations of neurons within specific anatomical brain regions, or the locations of sensors which measure neural activity (as in EEG). Certain important generic network properties turn out to depend solely on topological properties, independent of the details of individual network function. We illustrate this idea by discussing two simple intuitive properties, global and local efficiency of information transfer. For more complete discussions of network structure-function dependencies the reader is referred to several excellent recent reviews (Albert & Barabasi, 2000; Strogatz, 2001; Bassett & Bullmore, 2006; Stam & Reijneveld, 2007; Reijneveld et al., 2007; Sporns, 2010).
