Dynamic networks are categorised into four major types: Unweighted-Undirected, Weighted-Undirected, Unweighted-Directed and Weighted-Directed. Mining regular patterns in these dynamic networks finds extensive applications in prediction systems, analysing social network activity, etc. Depending on types of dynamic networks, regular patterns conforming to different characteristics of a system can be mined, each providing some unique information. Dynamic networks have pattern mining techniques that mine these characteristics individually. However, to the best of our knowledge, no such work is available that mines all these regular patterns (if possible) in a single run for any type of the dynamic networks. In this paper, the authors' focus is on providing a framework to make the task of mining regular patterns in dynamic networks more efficient and exhaustive. The accuracy of this framework has been verified mathematically and its efficiency is validated experimentally on a real-world network (Enron dataset).