This paper aims to analyze longitudinal data, serial data related to different time points, in knowledge graphs. Few studies have investigated how multiple layers and time points within graphs affect methods and algorithms developed for single-purpose networks. This manuscript investigates the impact of modeling longitudinal data in multiple layers on retrieval algorithms. In particular, (a) we propose a first draft of a generic model for longitudinal data in multi-layer knowledge graphs, (b) we develop an experimental environment to evaluate a generic retrieval algorithm on random graphs inspired by computational social science. We present a knowledge graph generated on German job advertisements containing data from different sources, both structured and unstructured, for the period between 2011 and 2021. The data is linked using text mining and natural language processing methods. We also (c) present two different shrinking techniques for structured and unstructured layers of knowledge based on graph structures such as triangles and pseudo-triangles. The presented approach (d) shows that, on the one hand, the initial research questions and, on the other hand, the graph structures and topology have a great impact on the structures and efficiency for additional stored data. Although the experimental analysis of random graphs allows us to make some basic observations, we will (e) make suggestions for additional research on certain graph structures that have a great impact on the analysis of knowledge graph structures.