The widespread adoption of smart phones allows for the seamless capture of social interactions on a scale that was once impossible. Co-presence, collected using Bluetooth on the phones, faithfully represents such real-world social interactions. This social information can be transformed into communities, which can be leveraged into applications such as recommender systems and collaborative tools. However, correctly identifying communities is difficult. This paper presents TIE, a visualization

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... ol that enables effective review of detected communities. With TIE, we can visualize the social interaction of a set of people over time. Also, TIE can overlay detected community events in a usable way over the underlying social interactions. Further, it allows us to investigate specific social interaction events and see how well detected communities match those events. Lastly, it enables the comparison of different sets of detected communities by interactively switching between overlays. TIE has proven useful in evaluating our community detection algorithms and has been invaluable in identifying strengths and weaknesses of these algorithms. Beyond our needs, TIE is usable for other data sets that can be reduced to temporal interaction events such as multiplayer game communities, SMS interactions, and paper co-authorship.