A Distributed Framework for Large-Scale Time-Dependent Graph Analysis

In the last few years, we have seen that many applications or computer problems are mobilized as a graph since this data structure gives a particular handling for some use cases such as social networks, bioinformatics, road networks and communication networks. Despite its importance, the graph processing remains a challenge when dealing with large graphs. In this context, several solutions and works have been proposed to support large graph processing and storage. Nevertheless, new needs are emerging to support the dynamism of the graph (Dynamic Graph) and properties variation of the graph during the time (temporal graph). In this paper, we first present the concepts of dynamic and temporal graphs. Secondly, we show some frameworks that treat static, dynamic and temporal graphs. Finally, we propose a new framework based on the limits of the frameworks study.