The temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We, therefore, extended the distributed graph analytics framework Gradoop for time-related graph analysis by introducing a new temporal property graph data model. Our model supports bitemporal time dimensions for vertices and edges to represent both rollback and historical information. In addition to the data model, we introduce several time-dependent operators (e.g, Snapshot, Diff and Grouping) that natively support the natural evolution of the graph. Since this is an extension of Gradoop, the temporal operators are compatible and can be combined with the already known operators to build complex analytical tasks in a declarative way. In our talk, we will give a brief overview of the Gradoop system, the temporal property graph model and how we support the time-dependent analysis of large graphs. Based on real-world use-cases, we show the expressiveness and flexibility of our temporal operators and how they can be composed to answer complex analytical questions.