Frequent Subgraph Mining (FSM) is an active research field and is considered as the essence of graph mining. FSM is extensively used in graph clustering, classification and building indices in the databases. In literature, different FSM approaches are suggested such as AGM, FSG, SPIN, SUBDUE, gSpan, FFSM, CloseGraph, FSG, GREW. Most of these FSM techniques perform very well for small to medium size graph datasets, but the computational cost of FSM becomes very critical when the graph size is increased. In accession to this, the number of frequent subgraphs patterns grows exponentially with the increasing size of graph datasets. Consequently, in this research work, a conceptual framework called A RAnked Frequent pattern-growth Framework (A-RAFF) is proposed. A-RAFF achieved efficiency by embedding the ranking of discovered frequent subgraphs during the mining process. The experiments on real and synthetic graph datasets demonstrated that the mining results of A-RAFF are very promising as compared to the existing FSM techniques.