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The paper investigates the optimal design of clustering in experimental setups, particularly in the context of social networks. It discusses theoretical frameworks, objective functions, and practical algorithms for choosing the best clustering method. The authors analyze the impact of various factors, including bias, variance, and spillover effects, providing recommendations for real-world applications.

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