to clusters of interest include methods developed for community detection [@fortunato_community_2010]. Many global community detection methods, such as modularity maximization [@newman_modularity_2006], have a resolution limit such that they do not distinguish small clusters [@fortunato_resolution_2007]; graph cluster randomization with these methods could then introduce too large an increase in variance for the resulting bias reduction. Therefore, local clustering methods may be more appealing for graph cluster randomization. Observed community membership (e.g., current educational institution) or geography could also be used as this mapping.

Lastly, it is important to note that independent random assignment can be considered as clustered random assignment where each vertex is in its own cluster.

Outcome generation and observation {#observed_outcomes}
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Given the network (along with vertex characteristics and prior behavior) and treatment assignments, some data generating process produces the observed outcomes of interest. In the context of social networks, typically this is the unknown process by which individuals make their decisions. In this work, we consider a variety of such processes. For our simulations, we use a known process meant to simulate decisions, in which units respond to others’ prior behaviors. Doing so allows us to understand the performance of varied design and analysis methods, measured in 