Hybrid systems are control-theoretic approach that combine both continuous time dynamics and discrete event logic. Hybrid systems enable complexity reduction through the continuous approximation of discrete variables and successfully have modeled physical systems, robots, vehicle control, etc. However, Hybrid systems are new to networking modeling, and we are the first researchers who applying these to model communication networks. To characterize network behavior, these models use averaging to continuously approximate discrete variables, such as congestion window and queue size. Because averaging occurs over short time intervals, one still models discrete events such as the occurrence of a drop and the consequent reaction (e.g., congestion control). The proposed hybrid systems modeling framework fills the gap between packet-level and fluid-based models: by averaging discrete variables over a very short time scale (on the order of a round-trip time). Thus, hybrid systems are able to capture the dynamics of transient phenomena fairly accurately. This provides significant flexibility in modeling various congestion control mechanisms, different queueing policies, multicast transmission, etc.