Diffusion of innovations theory can be used to understand how to prevent or slow the spread of harmful behaviors, such as e-cigarette use in adolescent social networks. This study explores how different network intervention strategies could impact diffusion dynamics through network simulations based on observed social norms and e-cigarette use data. Simulations were initialized with baseline network data collected from 10 schools in a prospective cohort study of adolescent social networks and health behaviors in Southern California. Diffusion conditions varied by changes in social norms for intervention nodes (pro-e-cigarette, anti-e-cigarette, or neutral norms) and intervention strategy, where greater pro- and anti-tobacco norms were assigned to 15% of the network based on four intervention seeding conditions: opinion leadership, betweenness centrality, segmentation, and random selection. For each network, simulations were run using the netdiffuseR package in R and multivariate generalized linear models were estimated to examine changes in diffusion dynamics. Diffusion prevalence and rate were greater in denser networks and networks with more initial e-cigarette users. Anti-e-cigarette norms significantly decreased average prevalence across all intervention conditions. Strategically selecting high betweenness centrality nodes and opinion leader nodes significantly decreased the average prevalence of e-cigarette use. The results of this study show that achieving a change in norms for 15% of a network can substantially impact e-cigarette use prevalence. Furthermore, this study enhances our knowledge of how personal and network factors affect diffusion dynamics and demonstrates that targeting social norms through network-based interventions is one avenue for slowing the spread of harmful behaviors.