Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses and update their own guess, and so forth. We study learning dynamics in three largescale networks capturing features of real-world social networks: Erdös-Rényi, Stochastic Block (reflecting network homophily) and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs. These patterns are consistent with the predictions of DeGroot updating. It lends support to the notion that the use of simple heuristics in information aggregation is prevalent in large and complex networks.