# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 3 Network Datasets and Experimental Settings
### Datasets
#### 3.1.2 Directed Networks  
The first directed network is constructed from the retweeting functionality of Twitter. There are predominantly two types of dynamic social networks that can be developed using the Twitter social network platform by considering its retweeting and mentioning mechanism. The first considers the fact that a Twitter user is reposting another user's tweet, and the latter considers one user mentioning the other in his/her tweet by using _@username_. Both types of networks built upon the Twitter information diffusion mechanism are directed networks. The Twitter network dataset in this study, collected from Network Repository, is a retweet network. Actors in this network are Twitter users, and a link  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Equation** & **Description** \\ \hline \(sim_{1}(a,b)\) & \(min(\sum_{l=1}^{L}d(\delta_{nl^{\prime}}^{a},\delta_{ml}^{b}))\) & Temporal similarity of structural dynamicity measured using Dynamic Time \\  & & Warping (DTW) Technique \\ \hline \(sim_{2}(a,b)\) & \(min(\sum_{l=1}^{L}d(\delta_{nl^{\prime}}^{a},\delta_{nl}^{b}))\) & Temporal similarity of neighbourhood dynamicity \\  & & measured using Dynamic Time Warping (DTW) Technique \\ \hline \(sim_{3}(a,b)\) & \(\frac{\sum_{l=1}^{L}[(\delta^{\prime}(t)-\overline{\delta^{\prime}})(\delta^{ \prime}(t)-\overline{\delta^{\prime}})]}{\sum_{l=1}^{L}[\delta^{\prime}(t)- \overline{\delta^{\prime}}]}\) & Correlation between structural dynamicity of two \\  & & non-connected actors computed using Pearson correlation \\ \hline \(sim_{4}(a,b)\) & \(\frac{\sum_{l=1}^{L}[(\delta^{\prime}(t)-\overline{\delta^{\prime}})(\delta^{ \prime}(t)-\overline{\delta^{\prime}})]}{\sum_{l=1}^{L}[\delta^{\prime}(t)- \overline{\delta^{\prime}}]^{2}}\) & Correlation between neighbourhood dynamicity of two non-connected actors \\  & & computed using Pearson correlation \\ \hline \(sim_{5}(a,b)\) & \(1-\frac{\sum_{l=1}^{L}[(\delta^{\prime}(t)-\delta^{\prime}(t))+|\lambda^{\prime }(t)-\lambda^{\prime}(t)|]}{\sum_{l=1}^{L}[|\delta^{\prime}(t)+\beta^{\prime} (t)|+|\lambda^{\prime}(t)+\lambda^{\prime}(t)|]}\) & Similarity by the abundance of structural and neighbourhood dynamicity between two non-connected actors computed using Bray–Curtis dissimilarity measure \\ \hline \end{tabular}
\end{table}
Table 1: Five different values of \(sim_{i}(a,b)\) computed by using five different dynamic similarity metrics. Each metric computes the similarity/proximity between non-connected actor pair \((a,b)\) by considering their structural (i.e., \(\delta^{a},\delta^{b}\)) and neighbourhood (i.e., \(\lambda^{a},\lambda^{b}\)) dynamicity computed in dynamic networks comprised of \(T\) SINs.  
between them denotes whether the users retweeted each other. Since all links in the retweet network are time-stamped, this study used three different sliding window sizes (i.e., \(\tau=6\), 12 and 24 h) to sample the longitudinal networks considering the temporal granularity of hours. For the sake of brevity, this study used \(G_{retw}\) to denote the retweet network in the rest of the study.