There are various methods for handling longitudinal data in graphs and social networks, all of which have an impact on the algorithms used in data analysis. This article provides an overview of limitations, potential solutions, and unanswered questions regarding different temporal data schemas in social networks that are comparable to existing techniques. Restricting algorithms to a specific time point or layer has no effect on the results. However, when applying these approaches to a network with multiple time points, adjusted algorithms or reinterpretation becomes necessary. Therefore, using a generic definition of temporal networks as one graph, we aim to explore how we could analyze longitudinal social networks with centrality measures. Additionally, we introduce two new measures, "importance" and "change", to identify nodes with specific behaviors. We provide case studies featuring three different real-world networks exhibiting both limitations and benefits of the novel approach. Furthermore, we present techniques to estimate variations in importance and degree centrality over time.