Evaluating methods for estimating influence effects in social networks: a simulation study

The contagion effect, also known as the peer effect, plays a significant role in social networks. It refers to the phenomenon in which one person or group can influence the behavior of other individuals with whom they share social connections. However, accurately estimating the contagion effect is challenging due to the confounding impact of peer selection. Previous studies have shown that this can be viewed as a problem of omitted variable bias. In an attempt to address this issue, simulation studies were conducted to compare the effectiveness of various methodologies in estimating peer influence within social networks, including latent variable models and machine learning techniques. Our research demonstrates that the performance of various approaches vary depending on the social network scenario. Overall, the latent space approach exhibits the best performance. This analysis provides valuable insights for researchers studying estimation techniques and emphasizes the importance of understanding the strengths, limitations, and objectives of these methods before using them for inference-based estimation.