There are currently several approaches to managing longitudinal data in graphs and social networks. All of them influence the output of algorithms that analyse the data. We present an overview of limitations, possible solutions and open questions for different data schemas for temporal data in social networks, based on a generic RDF-inspired approach that is equivalent to existing approaches. While restricting the algorithms to a specific time point or layer does not affect the results, applying these approaches to a network with multiple time points requires either adapted algorithms or reinterpretation. Thus, with a generic definition of temporal networks as one graph, we will answer the question of how we can analyse longitudinal social networks with centrality measures. We present two approaches to approximate the change in degree and betweenness centrality measures over time with two new measures, “importance” and “change”, to identify nodes with specific behaviors and apply these in two examples from educational research describing longitudinal data in labor market related topics in social networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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