In the EVIDEN project, we are interested in an emerging area in Information Visualization which deals with the exploration and the visual analysis of dynamic data. Our objective is to devise methods and algorithms for the visualization and navigation of dynamic and relational data. Following the visualization "pipeline", research in the EVIDEN project focuses on four main topics: 1/ Definition of a data structure that is versatile, flexible and optimized enough to store large dynamic and relational data. This data structure must be able to guarantee efficient access and update times. 2/ The design of methods for the decomposition and extraction of regions of interest in dynamic data. Decomposition methods must, on one hand, group similar elements and, on the other limit changes during data evolution. Methods to extract regions of interest must allow the detection and the extraction of sub-networks with atypical behaviours. 3/ The design of efficient methods for the visualization of dynamic data: we focus on two main topics, the visual representation of the dynamic data and the visual representation of data evolution. 4/ The design of interaction methods for dynamic data: adapting and/or formally redefining interaction methods for static data as in previous work is required by data evolution and dynamism. The types of problems that the EVIDEN project is addressing emerge naturally in the application domains of bioinformatics and biology. Ultimately, it aims at contributing to the Health sciences. Other application domains could also benefit from these results.