## 1. Introduction 

The human connectome represents a network map of the brain in which regions and inter-regional connections are rendered into the nodes and edges of a graph. In this format, the connectome can be analyzed using tools from network science and graph theory (Bullmore and Sporns, 2009; Sporns, 2014). Network analyses of the connectome have revealed a host of attributes that are likely essential for healthy brain function, including hierarchical and multi-scale modules (Bassett et al., 2010; Betzel et al., 2013), highly connected, highly central hubs (Hagmann et al., 2008; van den Heuvel and Sporns, 2013), and a rich club of mutually connected, high-degree regions (van den Heuvel and Sporns, 2011). Additionally, the connectome’s topology (the pattern in which its connections are configured) is thought to play an important role in shaping task-evoked and spontaneous brain activity (Hermundstad et al., 2013; Go˜ ni et al., 2014; Miˇsi´ c et al., 2015). 

The connectome is an example of a physical network whose nodes and edges are embedded in Euclidean space (Barth´ elemy, 2011). Consequently, the for- mation of connections carries a material and metabolic cost that increases with connection length (Bullmore and Sporns, 2012). To remain within the limits of viability, the human connectome maintains disproportionally many short-range (i.e. low cost) connections. Despite the importance of conserving connection cost, previous work in model organisms has shown that wiring minimization alone cannot account for all the connectome’s topological features (Kaiser and Hilgetag, 2006; Costa et al., 2007a). Rather, connectome networks strike a bal- ance wherein the formation of costly features like hubs and rich clubs trades off with a drive to reduce the total cost of wiring. 

The conditions that allow this tradeoff to emerge are the central topic of 

