Social networks play an important role in shaping individual decision-making and market outcomes. Capital market trading and pricing typically only incorporate a minimal amount of social interaction and information transmission, where an investor's opinions and actions only have a minimal impact on other investors' beliefs and behaviors through market price. Additionally, there is no localised contagion in trading and beliefs in conventional capital markets models. Trading habits do not spread from one investor to another in close proximity (either geographically or in other ways like socially, professionally, or through connectivity in the news media). Networks of social interaction among investors still need to be explored despite numerous studies on herding and information cascades. In recent years, there has been growing interest in using social network analysis to understand how information diffusion and behavioral biases spread through social networks and affect individual decision-making. This study builds on previous research by constructing an empirical investor network (EIN) based on investors’ trading behavior in the stock market. Using social network analysis techniques, we analyze the structure of the EIN and how it affects investors' trading behavior and returns. Specifically, we examine how behavioral biases spread through the network using the SIR model from epidemiology literature. Our findings provide valuable insights to individual investors, fund managers and financial regulators into how social networks influence individual decision-making and market outcomes.