this paper, and one that we explore using generative models applied to human connectome data obtained from individual participants. In the context of com- plex networks, generative modeling refers to a set of approaches for creating synthetic networks with properties similar to those of real-world networks. One example among many (Watts and Strogatz, 1998; Kumar et al., 2000; Sol´ e et al., 2002; V´ azquez et al., 2003; Dall and Christensen, 2002; Middendorf et al., 2005) is the preferential attachment model (Barab´ asi and Albert, 1999), which gener- ates synthetic networks with heavy-tailed degree distributions similar to those observed in many real-world socio-technical networks. In this report we build upon and expand the tradition of generative models for brain networks by fitting many different generative models to single-subject human connectome data and comparing models in terms of their overall per- formance. The models we investigate combine two distinct mechanisms for network growth: 1) geometric wiring rules which influence connection forma- tion by favoring either shorter or longer connections and 2) non-geometric rules that ignore the distance between two regions and, instead, form connections on the basis of some shared topological relationship. Some of the models we consider implement rules that mimic well-established growth mechanisms like geometric random graphs, preferential attachment, degree assortativity, and ho- mophilic attraction. In all cases, our aim is to discover wiring rules that produce synthetic networks with properties similar to those of observed connectomes. To this end, we tuned our models’ parameters to generate realistic synthetic networks. We found that the best-fitting model was one that penalized the formation of longer connections while increasing the likelihood of forming con- nections between brain regions with similar connectivity profiles (homophily). We cross-validated this result, comparing synthetic and observed connectomes along measures other than those used in the optimization process and using 3