# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 3 Network Datasets and Experimental Settings
### Supervised Link Prediction  
The primary objective of the link prediction mechanism is to analyse the intrinsic characteristics of the network in regard to topological information or attributes related to actors or links among them in the training phase \([t_{1},t^{\prime}]\) to predict the likelihood of future links in the test phase \([t^{\prime},t^{\prime}_{1}]\). Since the purpose of this study is to predict links in dynamic networks in longitudinal settings, we therefore split the network \(G_{T}\)\([t_{1},t^{\prime}]\) in the training phase into smaller temporal subnetworks considering different sliding window sizes to generate a time series or evolutionary sequence of network snapshots or SIN. Depending on the number of SINs in each dataset, the objective of this study is to build time series of two different dynamicity measures incident to actor pairs and generate dynamic features by considering the temporal similarity and correlation measures between two temporal sequences for the purpose of supervised link prediction. Supervised methods for link prediction problems need to predict possible future links by successfully discriminating between the links with positive and negative labels within a classification dataset. Thus, supervised link prediction turns into a binary classification task that involves learning positive and negative labels by exploiting interesting features describing each instance. Supervised link prediction setup starts with building classification datasets consisting of positive and negative instances. In this study's context, instances are the non-connected actor pairs from the network in the test phase \(G_{T+1}[t_{1},t^{\prime}]\). Instances are labelled as positive depending on their true appearances during the test phase, and links with negative labels were randomly selected from links that do not appear both in the training and test phases. This study considered a workload ratio of links with positive and negative labels.  
negative labels to 1:10. Thus, the number of negatively labelled links is ten times higher than those with positive labels in each classification dataset. However, in the case of the co-authorship networks, the workload ratio is 1:2. For the sake of simplicity in the link prediction problem, loops (i.e., links where source and destination are the same actors) were ignored and links that are unique in \(G_{T+1}\) are considered (i.e., links not present in \(G_{T}\). Choosing the appropriate feature set to describe instances in the classification dataset is one of the most important tasks in supervised link prediction. In each classification dataset of this study, both positively and negatively labelled actor-pair instances were described using features \(sim_{i}(v_{i},v_{j})\) (i.e., metrics summarized in Table 1) those were developed considering the similarity of the evolutionary information associated with each actor of a pair. This study constructed different classification datasets consisting of instances and dynamic features describing those instances. Depending on the number of sampling window sizes, for both directed and undirected networks, each network dataset had three classification datasets and altogether 15 classification datasets. In the case of the co-authorship network datasets, since there was only one sampling window size (i.e., one year), both datasets had one classification dataset defined for this study's purpose.