text: Many dynamic networks coming from real-world contexts are link streams, i.e. a finite collection of triplets @math where @math and @math are two nodes having a link between them at time @math . A very large number of studies on these objects start by aggregating the data in disjoint time windows of length @math in order to obtain a series of graphs on which are made all subsequent analyses. Here we are concerned with the impact of the chosen @math on the obtained graph series. We address the fundamental question of knowing whether a series of graphs formed using a given @math faithfully describes the original link stream. We answer the question by showing that such dynamic networks exhibit a threshold for @math , which we call the , beyond which the properties of propagation of the link stream are altered, while they are mostly preserved before. We design an automatic method to determine the saturation scale of any link stream, which we apply and validate on several real-world datasets.
  @cite_2: The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose a framework for identifying meaningful resolution levels that best reveal critical changes in the network structure, by balancing the reduction of noise with the loss of information. We demonstrate the applicability of our approach by analyzing various network statistics of both synthetic and real dynamic networks and using those to detect important events and changes in dynamic network structure.
summary: Contrastingly, the goal of @cite_2 is precisely to determine an ideal aggregation period. In their method, this period is obtained as a trade-off between two metrics that vary monotonically and oppositely with regard to aggregation: one describing the loss of information (increasing with aggregation) and one describing the noise contained in the series of snapshots (decreasing with aggregation). Compared to them, here, we are concerned only with the loss of information. This allows us to avoid some drawbacks and limitations inherent to the approaches based on a trade-off: i) the value selected for the aggregation period strongly depends on the importance given to each metrics and ii) the selected value does not reveal any particular behavior of the properties of the network used in the trade-off, as each of them varies smoothly and monotonically from one extremal value to another one. On the contrary, our method does not depend on any arbitrary choice of ponderation and reveals a natural change in the way the network responds to aggregation at a certain aggregation scale that we determine.