The hypothesis that violence—especially gang violence—behaves like a contagious disease has grown in popularity in recent years. Scholars have long observed the tendency for violence to cluster in time and space, but little research has focused on empirically unpacking the mechanisms that make violence contagious. In the context of gang violence, retaliation is the prototypical mechanism to explain why violence begets violence. In this study, we leverage relational event models (REMs)—an underutilized yet particularly well-suited modeling technique to study the dynamics of inter-gang violence. We use REMs to examine gang violence’s tendency to replicate—for which retaliation is but one plausible mechanism—and its tendency to diffuse to other groups. We rely on data on conflicts between gangs in a region of Los Angeles over 3 years. We consider how the characteristics of gangs, their spatial proximity, networks of established rivalries, and the evolving history, directionality, and structure of conflicts predict future inter-gang conflicts. While retaliation is an important mechanism for the replication of violence, established rivalries, and inertia—a gang’s tendency to continue attacking the same group—are more important drivers of future violence. We also find little evidence for an emerging pecking order or status hierarchy between gangs suggested by other scholars. However, we find that gangs are more likely to attack multiple gangs in quick succession. We propose that gang violence is more likely to diffuse to other groups because of the boost of internal group processes an initial attack provides.

David Gibson’s (2008) examination of research on conversational interaction highlighted methodological and theoretical gaps in current understanding – particularly around the localized construction of interaction and the reproduction of social structures. This paper extends extant formal models used by group process researchers to explain how exogenous status structures shape local interaction by incorporating insights from qualitative work examining the local production of conversational interaction. Relational events serve as a bridge between conversation analytic understandings of the deep structure of conversation and expectation states formal models of permeation. We propose a theoretical integration of the status organizing process (permeation) and local turn-taking rules (deep structure) as a more complete model of conversational behavior in task groups. We test a formalized construction of this preliminary theory by examining turn-taking using data from 55 task groups whose members vary in gender, authority, and legitimacy of that authority. This integrated model offers substantial improvements in prediction accuracy over using status information alone. We then propose ways to expand the integrated theoretical framework to advance current understandings of action and events in conversation. Finally, we offer suggestions for insights from group processes theories that could be incorporated into network models of interaction outside of this theoretical framework.

Face-to-face interactions in social groups are a central aspect of human social lives. Although the composition of such groups has received ample attention in various fields—e.g., sociology, social psychology, management, and educational science—their micro-level dynamics are rarely analyzed empirically. In this article, we present a new statistical network model (DyNAM-i) that can represent the dynamics of conversation groups and interpersonal interaction in different social contexts. Taking an actor-oriented perspective, this model can be applied to test how individuals’ interaction patterns differ and how they choose and change their interaction groups. It moves beyond dyadic interaction mechanisms and translates central social network mechanisms—such as homophily, transitivity, and popularity—to the context of interactions in group settings. The utility and practical applicability of the new model are illustrated in two social network studies that investigate face-to-face interactions in a small party and an office setting.