Predicting groups of people who are jointly deceptive is critical in settings such as sales pitches and negotiations. Past work on deception in videos focuses on detecting single deceivers and uses facial or visual features only. We propose the concept of Face-to Face Interaction Networks (FFINs) and Negative Interaction Networks (NINs) to model interactions within a group of people. The use of FFINs and NINs in this paper enables us to leverage network relations in predicting face-to-face deception for the first time. We will use a dataset of 185 videos from a deception-based game called Resistance for deception based prediction using FFINs. We first characterize the behavior of individuals, pairs, and groups of deceptive participants compared to non-deceptive participants. Our analysis reveals that less engaged deceivers are identified early. Moreover, pairs of deceivers tend to avoid mutual interaction and focus their attention on non-deceivers. In contrast, non-deceivers interact with everyone equally. We propose the notion of Negative Interaction Networks (NINs) and create a belief propagation neural net algorithm called BPNN based on dynamic FFINs and NINs to detect deceivers from videos that are just 1 minute long. We show that our method outperforms recent state-of-the-art computer vision, graph embedding, and ensemble methods by at least 20.9% AUROC in identifying deception from videos.