Given a dataset comprising a temporal sequence of communications between actors, how can we visualize the ‘flow’ of communication over time? Current practice transforms the dataset into a dynamic graph – vertices represent the actors and directed edges represent the communications. The directed edges are added and removed over time. There are then several approaches to visualizing dynamic graphs that optimize aesthetic criteria, most producing animated node-link diagrams. However, dynamic graphs are not the only way to model this problem. One alternative from the field of distributed computing is vector clocks. Recent work employed vector clocks to analyze communication flow in social networks with much effect, arguing that they provide new insights into the problem. In this paper, we use vector clocks as a basis for visualizing communication flow. We show that communication patterns, e.g., random, partitioned and core-periphery, are easily discernible in the resulting visualizations. We also argue that, in the cases where vector clocks are used to analyze communication flow, it is most natural to base the accompanying visualizations on vector clocks also.