14 nodes were, generally, rather small for both models. 3.4. Application to human lifespan data In addition to quantifying models’ performances, we asked whether the pa- rameters of the generative models captured meaningful information about in- dividual differences in network organization. To demonstrate the utility of the network modeling approach for characterizing individual variation, we extended our analysis to the NKI dataset’s N = 126 participants, spanning a range of ages from 7-85 years. With an average network density of 10%, a number of individual’s connectomes were fractured into multiple disconnected components (71 of the 126 participants). However, the largest connected component, across all participants, included 98 . 5 ± 0 . 03 percent of all nodes, indicating that in the majority of cases the network is divided into two components: one singleton node and a component containing all other nodes. We hypothesized that age- related changes in network organization may be captured by the parameters of the generative models, η and γ . We tested this hypothesis by first regressing out participants’ intracranial volumes and mean framewise displacement from parameter values obtained from the best-fitting MI models and correlating the residuals with participant age. We also expressed energies and KS statistics as z-scores relative to a generative model in which connections were formed ran- domly to correct for variations in network density with age (Betzel et al., 2014; Lim et al., 2015). This null model preserved only the density of connections and not degree sequence. The results of these analyses indicated that the value of η decreased in magnitude with age (ˆ r$_{age,η}$ = 0 . 39 , p ≈ 10 − 5 . $^{3}$), while γ did not exhibit any significant age-related changes (ˆ r$_{age,γ}$ = 0 . 07 , p ≈ 0 . 45), which implied that the penalty on long-distance connections weakened with age. We also found that E , KS$_{e}$ , and KS$_{c}$ all increased with age (max p ≈ 10 − 4 . $^{7}$) (Fig- ure 5), indicating that the MI model does an increasingly poor job capturing