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_38: Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network given previous snapshots. We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling. We also introduce a novel generative model of noise in dynamic networks that represents oversampling. We demonstrate the results of our approach on both synthetic and real-world data.
  @cite_37: We study the problem of determining the proper aggregation granularity for a stream of time-stamped edges. Such streams are used to build time-evolving networks, which are subsequently used to study topics such as network growth. Currently, aggregation lengths are chosen arbitrarily, based on intuition or convenience. We describe ADAGE, which detects the appropriate aggregation intervals from streaming edges and outputs a sequence of structurally mature graphs. We demonstrate the value of ADAGE in automatically finding the appropriate aggregation intervals on edge streams for belief propagation to detect malicious files and machines.
  @cite_22: The topology of social networks can be understood as being inherently dynamic, with edges having a distinct position in time. Most characterizations of dynamic networks discretize time by converting temporal information into a sequence of network "snapshots" for further analysis. Here we study a highly resolved data set of a dynamic proximity network of 66 individuals. We show that the topology of this network evolves over a very broad distribution of time scales, that its behavior is characterized by strong periodicities driven by external calendar cycles, and that the conversion of inherently continuous-time data into a sequence of snapshots can produce highly biased estimates of network structure. We suggest that dynamic social networks exhibit a natural time scale nat , and that the best conversion of such dynamic data to a discrete sequence of networks is done at this natural rate.
  @cite_21: 
  @cite_44: 
  @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.
  @cite_34: 
  @cite_17: Temporal streams of interactions are commonly aggregated into dynamic networks for temporal analysis. Results of this analysis are greatly affected by the resolution at which the original data are aggregated. The mismatch between the inherent temporal scale of the underlying process and that at which the analysis is performed can obscure important insights and lead to wrong conclusions. To this day, there is no established framework for choosing the appropriate scale for temporal analysis of streams of interactions. Our paper offers the first step towards the formalization of this problem. We show that for a general class of interaction streams it is possible to identify, in a principled way, the inherent temporal scale of the underlying dynamic processes. Moreover, we state important properties of these processes that can be used to develop an algorithm to identify this scale. Additionally, these properties can be used to separate interaction streams for which no level of aggregation is meaningful versus those that have a natural level of aggregation.
summary: It is paradoxical to note that, while the question of the influence of aggregation on the properties of the formed graph series is largely ignored in most of the studies on dynamic networks, this question actually already received a lot of specific attention @cite_34 @cite_21 @cite_44 @cite_2 @cite_22 @cite_37 @cite_38 @cite_17 .