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Existing continuous\u2010time methods for modeling such data are based on point processes and directly model interaction \u201ccontagion,\u201d whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal\u2010relational point process models for continuous\u2010time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well\u2010established underlying social relationships. 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