The community detection play an important role in understanding the information underlying to the graph structure, especially, when the graph structure or the weights between the linked entities change over time. In this paper, we propose an algorithm for the community identification in weighted and dynamic graphs, called Dyci. The latter takes advantage from the previously detected communities. Several changes’ scenarios are considered like, node/edge addition, node/edge removing and edge weight update. The main idea of Dyci is to track whether a connected component of the weighted graph becomes weak over time, in order to merge it with the “dominant” neighbour community. In order to assess the quality of the returned community structure, we conduct a comparison with a genetic algorithm on real-world data of the ANR-Info-RSN project. The conducted comparison shows that Dyci provides a good trade-off between efficiency and consumed time.