Heritability of network characteristics in a real social network (Add Health) and simulated networks based on 5 models, the Attract and Introduce model; a social space model (); a “fitness” model (); an exponential random graph model (ERGM) (); and an Erdos–Renyi random network (). We used additive genetic models of monozygotic and dizygotic twins in Add Health to measure the heritability of network characteristics in real human social networks (see SI). The blue bars show that genetic variation accounts for significant variation in in-degree, transitivity, and betweenness centrality (vertical lines indicate 95% confidence intervals, asterisks indicate which confidence intervals exclude 0). These results suggest intrinsic characteristics have an important impact on the fundamental building blocks of real human social networks. Heritability of out-degree is not significant. To see which network models are capable of generating heritability consistent with the empirical observations, we simulated 10,000 pairs of networks and used the “mirror network” method for each proposed model to measure how much variance in network measures can be explained by intrinsic node characteristics. Compared with heritability estimates from the real social network data, all proposed models are rejected because they fall outside the confidence intervals except ERGM for in-degree and transivity, and Attract and Introduce for all three significantly heritable network properties (in-degree, transitivity, and centrality).

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