Snapshot Setting for Temporal Networks Analysis

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 275)


Temporal networks can be used to model systems that evolve over longer time scales such as networks of disease spread, for instance, HIV/AIDS disease that is propagated within the population over a relatively long period. Analyzing temporal networks can be done by considering the network either as a series of snapshots (aggregation over a time window) or as a dynamic object whose structure changes over time. The first approach is used in this paper and requires specifying a size of time window that delimits snapshot size. To our best knowledge, there is not yet studies on setting the size of the window in a methodical basis. In real, existing works rely on a static or a regular value of time window size to capture snapshots over time.

This work is conducted to identify dynamically snapshots over time in a directed and weighted network. That is, we aim to find out the right time to start and to end capturing a new snapshot. To this end, we define a quality function to evaluate the network state at anytime. Then, we rely on time series to predict the quality scores of the network over time. A significant changes of the network state is interpreted as the start and/or end of a snapshot. Our solution is implemented with R and we use a real dataset based on geographical proximity of individuals to demonstrate the effectiveness of our approach.


Time window size Temporal networks Quality function 


  1. 1.
    Ould Mohamed Moctar, A., Sarr, I.: Ego-centered community detection in directed and weighted networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 1201–1208, New York, NY, USA, 2017. ACM (2017)Google Scholar
  2. 2.
    Génois, M., Vestergaard, C.L., Fournet, J., Panisson, A., Bonmarin, I., Barrat, A.: Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Network Sci. 3(3), 326–347 (2015)CrossRefGoogle Scholar
  3. 3.
    Holme, P.: Modern temporal network theory: a colloquium. Eur. Phys. J. B 88(9), 234 (2015)CrossRefGoogle Scholar
  4. 4.
    Krings, G., Karsai, M., Bernhardsson, S., Blondel, V.D., Saramäki, J.: Effects of time window size and placement on the structure of an aggregated communication network. EPJ Data Sci. 1(4), 1–16 (2012)Google Scholar
  5. 5.
    Madan, A., Cebrian, M., Moturu, S., Farrahi, K., et al.: Sensing the “health state” of a community. IEEE Pervasive Comput. 11(4), 36–45 (2012)CrossRefGoogle Scholar
  6. 6.
    Psorakis, I., Roberts, S.J., Rezek, I., Sheldon, B.C.: Inferring social network structure in ecological systems from spatio-temporal data streams. J. R. Soc. Interface 9, 3055–3066 (2012)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceCheikh Anta Diop UniversityDakarSenegal
  2. 2.Department of Mathematics and Computer ScienceUniversity of NgaoundereNgaoundereCameroun

Personalised recommendations