论文信息 - Snapshot Setting for Temporal Networks Analysis

Snapshot Setting for Temporal Networks Analysis

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.

Idrissa Sarr | Joel Tanzouak | Ahmed Ould Mohamed Moctar

[1] Alex Pentland,et al. Sensing the "Health State" of a Community , 2012, IEEE Pervasive Computing.

[2] Iead Rezek,et al. Inferring social network structure in ecological systems from spatio-temporal data streams , 2012, Journal of The Royal Society Interface.

[3] Idrissa Sarr,et al. Ego-centered community detection in directed and weighted networks , 2017, ASONAM.

[4] Jari Saramäki,et al. Effects of time window size and placement on the structure of an aggregated communication network , 2012, EPJ Data Science.

[5] Petter Holme,et al. Modern temporal network theory: a colloquium , 2015, The European Physical Journal B.

[6] Julie Fournet,et al. Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers , 2014, Network Science.