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Inspecting dynamics of networks opens up a new dimension in the understanding or mechanisms behind real-world systems. Involving the time factor may help identifying previously hidden (or otherwise hard to recognize) phenomenon and/or patterns compared to static analysis, like individuals periodically changing between groups within a community.
Concentrating on edge dynamics, we defined a set of dynamic network models with various rules (including creating new and relinking edges randomly, by using assortative mixing or preferential attachment strategies) to analyize the evolution of different network properties. Starting from an initial network created by classical network models (like the Erdos-Renyi model) we examined the evolution of basic structural network properties (including density, clustering, average path length, number of components, degree distribution and betweennes centralities). The structure of the snapshot network (i.e., the network that is actually observed in a given instant of time) and the cumulative network (i.e., the network that is constructed by collecting and aggregating several samples of snapshot networks over a period of time) is inherently different, but we also found that certain properties have a strong dependence on the sampling windows length: we made experiments through computer simulations with various aggregation time windows and found that it has a great impact on the results.
In our presentation, we would like to briefly introduce the key findings of our previous results regarding to the elementary dynamic network models, and compare the theoretical results obtained from evaluating different empirical data sets. The selected data sets used for the comparison include political event data compiled from English-language news reports and a dataset created to analyze internet-mediated sexual encounters in Brazil.