Contagion Effect

The contagion effect describes how behaviors, failures, or other phenomena spread through networks, impacting individuals beyond initial exposure points. Current research focuses on improving the accuracy and interpretability of contagion models, employing techniques like graph neural networks, agent-based modeling, and diffusion models to capture complex interactions and account for confounding factors such as homophily. These advancements are crucial for applications ranging from financial risk assessment and epidemic forecasting to understanding information diffusion in social and organizational networks, ultimately enabling better prediction and mitigation strategies. The development of high-quality datasets and explainable models is a key area of ongoing effort.