Several data-intensive applications take streams of events as a continuous input and internally map events onto a dynamic, graph-based data model which is then used for processing. The differences between event processing, graph computing, as well as batch processing and near-realtime processing yield a number of specific requirements for computing platforms that try to unify theses approaches. By combining an altered actor model, an event-sourced persistence layer, and a vertex-based, asynchronous programming model, we propose a distributed computing platform that supports event-driven, graph-based applications in a single platform. Our Chronograph platform concept enables online and offline computations on event-driven, history-aware graphs and supports different processing models on the evolving graph.