Real-world systems from a variety of domains, ranging from physics to medicine, can naturally be modelled as dynamic networks. Dynamic community detection is regarded as a fundamental tool to probe into the mechanisms of networks. Here, we describe a framework for tracking the network evolution over time, where each community is characterized by a series of transition events, which is one of the most influential evolutionary patterns in dynamic networks. The framework is used to motivate a temporal smoothness strategy for efficiently identifying dynamic communities and exploring the transition behavior of networks from community-level and node-level. Evaluations on two synthetic and real-world datasets containing embedded transition events demonstrate that the framework can successfully discover dynamic communities and analyze the transition behavior of networks.