In the past decades, the life course perspective and the vulnerability framework have grown in popularity to study how risks spread across people lives. Such studies involve complex longitudinal and network data as well as specific analysis methods. This thesis aims to help the social scientist in managing and analyzing such data. To support the provided methodological contributions, the thesis starts by setting a conceptual model of the diffusion of vulnerability along the life course. Then, the thesis develops several complementary strategies for exploring the set of vulnerability descriptive variables with the aim to identify interaction effects, such as when the gender effect depends on the age. The strategies rely on classification trees and specifically focus on unexpected interaction effects and data imbalance. In an illustrative application focusing on vulnerability to poverty, the proposed methods successfully achieved to identify an unexpected interaction effect between ego's and father's educational resources on ego's unemployment. Several of the contributions are made available to the scientific community through a series of R packages.