Snapshot Setting for Temporal Networks Analysis
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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.
KeywordsTime window size Temporal networks Quality function
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