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In this paper, we demonstrate the importance\r\nof explicitly accounting for unobserved heterogeneity in exponential random graph\r\nmodels (ERGM) with a Monte Carlo analysis and two applications that have played\r\nan important role in the networks literature. Overall, these analyses show that failing\r\nto account for unobserved heterogeneity can have a significant impact on inferences\r\nabout network formation. The proposed frailty extension to the ERGM (FERGM)\r\ngenerally outperforms the ERGM in these cases, and does so by relatively large mar-\r\ngins. 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