# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 3 Network Datasets and Experimental Settings
### Performance Evaluation  
As mentioned earlier, this study utilized dynamic features, computing similarity or correlation between two actors by considering network dynamics and using their evolutionary information, as described in Section 2, to describe both positively and negatively labeled instances in the classification datasets. Dynamic feature values were normalized such that the distribution has zero mean and one standard deviation. In regard to classifiers, this study used simple logistic regression, Random Forest, and Bagging algorithms. The latter two algorithms use ensemble-based methods. The well-known machine learning library WEKA [53] was used for classification purposes using the default parameters. For validation purposes, this study considered 10-fold cross-validation, and the mean scores \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Actors**} & \multirow{2}{*}{**Links**} & \multicolumn{2}{c}{**Training Duration**} & \multicolumn{2}{c}{**Testing Duration**} & \multicolumn{2}{c}{**Sampling Interval**} & \multicolumn{1}{c}{**SINs**} \\ \cline{3-8} \cline{6-9}  & & & **Start** & **End** & **Start** & **End** & \(\tau\) & & \\ \hline \multirow{3}{*}{\(G_{MIT}\)} & \multirow{3}{*}{96} & \multirow{3}{*}{1,086,404} & \multirow{3}{*}{14 September 2004} & \multirow{3}{*}{31 January 2005} & \multirow{3}{*}{1 February 2005} & \multirow{3}{*}{5 May 2005} & \multirow{3}{*}{7 days} & \multirow{3}{*}{20} \\ \cline{4-4} \cline{8-9}  & & & & & & 30 days & 5 \\ \cline{4-4} \cline{8-9}  & & & & & 1 day & 186 \\ \cline{4-4} \cline{8-9}  & & & & & 1 day & 186 \\ \cline{4-4} \cline{8-9}  & & & & & 7 days & 31 \\ \cline{4-4} \cline{8-9}  & & & & & 30 days & 8 \\ \hline \multirow{3}{*}{\(G_{UCI}\)} & \multirow{3}{*}{1899} & \multirow{3}{*}{61,734} & \multirow{3}{*}{24 March 2004} & \multirow{3}{*}{31 May 2004} & \multirow{3}{*}{1 June 2004} & \multirow{3}{*}{26 October 2004} & \multirow{3}{*}{7 days} & \multirow{3}{*}{7} \\ \cline{4-4} \cline{8-9}  & & & & & 30 days & 3 \\ \cline{4-4} \cline{8-9}  & & & & & 1 day & 90 \\ \cline{4-4} \cline{8-9}  & & & & & 7 days & 13 \\ \cline{4-4} \cline{8-9}  & & & & & 30 days & 3 \\ \hline \multirow{3}{*}{\(G_{FF}\)} & \multirow{3}{*}{11,715} & \multirow{3}{*}{42,698} & \multirow{3}{*}{1 January 2007} & \multirow{3}{*}{31 March 2007} & \multirow{3}{*}{1 April 2007} & \multirow{3}{*}{30 April 2007} & \multirow{3}{*}{7 days} & \multirow{3}{*}{13} \\ \cline{4-4} \cline{8-9}  & & & & & 30 days & 3 \\ \hline \multirow{3}{*}{\(G_{retret}\)} & \multirow{3}{*}{14,370} & \multirow{3}{*}{39,124} & \multirow{3}{*}{14 September 2010} & \multirow{3}{*}{14 October 2010} & \multirow{3}{*}{14 October 2010} & \multirow{3}{*}{15 October 2010} & \multirow{3}{*}{12 h} & \multirow{3}{*}{61} \\ \cline{4-4} \cline{8-9}  & & & 4 a.m. & 4 a.m. & 4 a.m. & 4 a.m. & 24 h & 31 \\ \hline \multirow{3}{*}{\(G_{\rm fil}\)} & \multirow{3}{*}{6798} & \multirow{3}{*}{290,597} & \multirow{3}{*}{1 October 1993} & \multirow{3}{*}{31 December} & \multirow{3}{*}{1 January 1999} & \multirow{3}{*}{1 December} & \multirow{3}{*}{1 year} & \multirow{3}{*}{6} \\ \cline{4-4} \cline{8-9}  & & & & 1998 & & 1999 & & & \\ \hline \multirow{3}{*}{\(G_{\rm fil}\)} & \multirow{3}{*}{16,959} & \multirow{3}{*}{2,322,259} & \multirow{3}{*}{15 March 1992} & \multirow{3}{*}{31 December} & \multirow{3}{*}{1 January 1999} & \multirow{3}{*}{31 December} & \multirow{3}{*}{1 year} & \multirow{3}{*}{7} \\ \cline{4-4} \cline{8-9}  & & & & 1998 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic statistics of network datasets used in this study. The training duration represents the interval used to generate temporal short-interval networks and the sampling interval denotes the sliding window sizes used to sample dynamic networks. SINs represent the number of short-interval networks or network snapshots generated using the corresponding window size. were used to determine the accuracy of the results.