Dynamic network data are now available in a wide range of contexts and domains. Several representation formalisms exist to represent dynamic networks, but there is no well-known method to choose one representation over another for a given dataset. In this article, we propose a method based on data compression to choose between three of the most important representations: snapshots, link streams and interval graphs. We apply the method on synthetic and real datasets to show the relevance of the method and its possible applications, such as choosing an appropriate representation when confronted to a new dataset, and storing dynamic networks in an efficient manner.

翻译：动态网络数据目前可在多种环境和领域获得。 存在几种代表形式形式来代表动态网络,但并不存在为特定数据集选择一种代表比另一种代表的已知方法。 在本条中,我们提议了一种基于数据压缩的方法,以在三个最重要的表达形式中作出选择:快照、链接流和间隔图。我们使用合成和真实数据集的方法来显示该方法的相关性及其可能的应用,例如,在面对新的数据集时选择适当的代表,以及有效地存储动态网络。