Recently, graph clustering has become one of the most used techniques to understand structures and inherent knowledge in graph data. This trend progressively attracts the attention of companies and the research community. For example, in the industrial field, it is used for multiple applications like social networks (e.g. Facebook), where communities can be modeled as clusters in a graph. As for collaborative networks (e.g. DBLP), a cluster can represent a team with similar research interests. Several works have been established where their proposed approaches are based on advanced algorithms mainly graph clustering algorithms and modularity based-ones. The former has demonstrated their efficiency notably by providing supplementary information about clusters in a list. Besides, they can identify hub and outlier vertices. Despite their importance, the utility of such algorithms is limited by their high complexity particularly when dealing with Big and dynamic graphs. This limitation motivates us to propose new algorithms with higher performances in our thesis. For more details, our contributions can be summarized in the following points: (1) carrying out of a comparative study between the most popular Big Data platforms (2) proposing a distributed algorithm called DSCAN for large graphs clustering and (3) extending DSCAN to develop an incremental algorithm for dynamic and large graphs. A comparative study between our proposed algorithms and other baselines has shown their effectiveness and their scalability when dealing with large and dynamic graphs
Keywords: Dynamic graph clustering, graph mining, community detection, Big Data, graph clustering, big graph processing