A distributed and incremental algorithm for large-scale graph clustering

Graph clustering is one of the key techniques to understand structures that are presented in networks. In addition to clusters, bridges and outliers detection is also a critical task as it plays an important role in the analysis of networks. Recently, several graph clustering methods are developed and used in multiple application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on the structural clustering algorithm. Yet, this kind of algorithm is based on the structural similarity. This latter requires to parse all graph’ edges in order to compute the structural similarity. However, the height needs of similarity computing make this algorithm more adequate for small graphs, without significant support to deal with large-scale networks. In this paper, we propose a novel distributed graph clustering algorithm based on structural graph clustering. The experimental results show the efficiency in terms of running time of the proposed algorithm in large networks compared to existing structural graph clustering methods. • An adaptation of the edge partitioning method in a distributed setting. • A novel scalable clustering method for distributed networks. • An incremental graph clustering algorithm for both large and dynamic graphs. • An experimental study to evaluate the novel scalable clustering method for distributed networks.