The problem of community detection role in analysis of complex large-scale networks and behavioral and engineering sciences. Examples of sue clustering) in graphs plays an important big data structures, arising in natural works include, but are not limited World Wide Web (WWW) and Internet, social networks, ecological networks and food webs, cellular and molecular ensembles. A community (or a module) in a graph is a subset of its nodes, whose members are "densely" connected to each other yet have relatively few connections with nodes outside this subset. A number of algorithms to subdivide the nodes of large scale graphs into communities have recently been proposed; Many of them Hint for the graph's partitions of maximal modularity. One of the most, efficient, graph clustering algorithms of this type is the Multi-Level Aggregation (or "Louvain") method. In this paper, a randomized counterpart of this algorithm is proposed, which provides a comparable "quality" of graph's clustering, being however much faster on huge graphs. We demonstrate the efficiency of our algorithm, comparing its performance On several "benchmark" large-scale graphs with existing methods. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.