Graphs are widely employed in massive scale social network analysis. Graph mining more and more necessary in modeling difficult structures like circuits, images, web, biological networks and social networks. The key issues occur during this graph mining are machine potency (CE) and frequent sub graph mining (FSM). Machine potency describes the extent to that the time, effort or potency that use computing technology in IP. Frequent Sub graph Mining is that the mechanism of candidate generation while not duplicates. FSM faces the matter on numeration the instances of the patterns within the dataset and numeration of instances for graphs. The most objective of this project is to handle atomic number 58 and FSM issues. The paper refer to within the reference proposes associate degree formula referred to as Mirage formula to unravel queries exploitation sub graph mining. The planned work focuses on enhancing associate degree unvarying MapReduce based mostly Frequent Sub graph mining formula (MIRAGE) to contemplate optimum machine potency. The check information to be thought-about for this mining formula may be from any domains like medical, text and social data's (twitter).The major contributions are: associate degree unvarying Map Reduce based mostly frequent sub graph mining formula referred to as MIRAGE won't to address the frequent sub graph mining drawback. Machine potency is going to be enlarged through MIRAGE formula over Matrix Vector Multiplication. Performance of the MIRAGE are going to be incontestable through totally different artificial likewise as world datasets. The most aim is to improvise the prevailing formula to boost machine potency. .