Dynamic graphs are commonly used for describingnetworks with a time evolution. Shimada et al. [1] recently proposed to transform these graphs into a collection of signals indexed by vertices. This approach is here further explored in a number of different directions. First, the importance of a good indexing of a graph is stressed, and a solution is proposed using a node labeling algorithm which follows the structure of the graph. Second, a spectral analysis of identified signals is performed to compute features linked to graph properties such as regularity or structure in communities. Finally, these features can be tracked over time to evidence the structure evolution of the graph. As a case study, the approach is applied to a dynamic graph based on a dataset of trips made using the bike sharing system V ́elo'v in use in Lyon, France. This is shown to offer specific insights on behaviors of bike users over time in two districts of the city.