Nowadays, large number of applications of graph clustering are available, with expanding the span of the graph the conventional methods of clustering are not appropriate to manipulate this issue which are costly for computation. So that, it is necessary to get a good algorithm to tackle this problem. Graph clustering algorithms are considered as the most effective techniques for solving various partitioning problems. Global graph clustering which based on the whole graph as input isn’t convenient of large graphs. Local graph clustering algorithms solve this problem by working on a given vertex as input seed set without looking at the whole graph to find a good cluster. This research explores different graph clustering techniques based on the input parameters, e.g., local and global, as well as illustrating appropriate applications of graph clustering. This paper directed to help new researchers take a summary of graph clustering techniques that can be used for graph partitioning.