In this article we propose a data model and query language for the visualisation and exploration of topic evolution networks representing the research progress in scientific document archives. Our model is independent of a particular topic extraction and alignment method and proposes a set of semantic and structural metrics for characterizing and filtering meaningful topic evolution patterns. These metrics are particularly useful for the visualization and the exploration of large topic evolution graphs. We also present a first implementation of our model on top of Apache Spark and experimental results obtained for four real-world document archives.