the organization of older connectomes compared to younger connectomes. 

## 4. Discussion 

In this report, we tested different classes of generative models for the hu- man connectome. Our study makes several novel contributions, by quantita- tively comparing different sets of generative models, by applying these models to human connectome data, and by fitting models to networks of individual participants. We confirmed that pure geometric models of the form consid- ered in this report cannot create synthetic networks that were both as clustered and also contained the same proportion of long-distance connections as the observed human connectome. To identify which additional factors were most capable of creating realistic networks we incorporated non-geometric informa- tion into our generative models’ wiring rules. With this additional degree of freedom, the synthetic networks generated by these more complex models more accurately reproduced the connectome’s clustering and edge length distribu- tions. The best-fitting model formed connections on the basis of homophilic attraction (matching index) combined with geometric constraints. Importantly, synthetic networks generated by this model not only reproduced degree, be- tweenness centrality, clustering coefficient, and edge length distributions (all measures that contributed to the energy function used for optimization), but they also reproduced additional graph theoretic properties such as character- istic path length, mean clustering coefficient, global efficiency, modularity, the propensity for high-degree nodes to be connected via long-distance edges, and local node statistics such as degree and clustering coefficient sequences. We also demonstrated robustness of the matching index model, comparing it across three separate datasets totaling N = 380 participants and finding consistent results in all cases (See Appendix). As a final demonstration of the utility of 

