Summary Cascade networks are an example of networks that connect individuals on the basis of the direction in which the data or information flows between them. These networks have garnered the attention of various sociologists interested in the diffusion of innovation for many years. Research goals have shifted over time and across platforms, from simply seeing and counting cascades to tracking, anticipating flow of information, and modeling them. Thus, understanding cascades is a crucial step in gaining a better understanding of how information spreads. This chapter will give an overview of the cascading behavior. It begins with an introduction to networks and the graph theory then move on to discuss cascade networks in depth along with their purpose and significance. The concepts of centrality, cascading failure, and cascading capacity are also covered. The different models that are discussed are decision-based models, probabilistic models, independent cascade model, linear threshold model, and Susceptible, Infectious, or Recovered (SIR) model. In the end, cascading behavior is explained using python codes to illustrate the practical applications of this concept. The focus is on the application of this concept to various substantive examples.