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Fig. 3.
Typical degree distribution results for the elementary dynamic network models.
Frequency is shown on the
z
axis, while how the distribution changes over time can be
read from the left to the right on each chart. The picture on the left hand side obtained
by an ER1 model shows the case we found for most of the models. The picture on the
right hand side obtained by a DoubleCPA model shows a more spread out distribution
which is the characteristic of the DoubleCPA and CPA models.
that it can be stabilized to an extent by using bidirectional preferential attach-
ment. Figure 3. demonstrates a typical result for most the models and for the
preferential ones.
3 Comparison against Empirical Data
After we got an overall insight on the evolution of properties of the introduced
models, the question is how far are these theoretical results from the proper-
ties of real-world systems? In order to answer that, we turned our attention to
the evaluation of publicly available and well-known empirical temporal network
datasets. In the following sections we briefly describe these datasets and the
results we obtained through the approach of dynamic analysis.
3.1 The Gulf Dataset
The Gulf dataset
“covers the states of the Gulf region and the Arabian peninsula
for the period 15 April 1979 to 31 March 1999. The Kansas Event Data Sys-
tem used automated coding of English-language news reports to generate political
event data focusing on the Middle East, Balkans, and West Africa. These data
are used in statistical early warning models to predict political change. The ten-
year project is based in the Department of Political Science at the University of
Kansas; it has been funded primarily by the U.S. National Science Foundation.
