# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 4 Results
### Classification Performance  
Table 3 sets out the performance scores of three different classifiers in classifying positive and negatively labeled links. Classifier performances are demonstrated considering three different performance metrics described before. In regard to the accuracy score, this study observes that both linear (i.e., logistic regression) and ensemble-based classifiers perform reasonably well using the dynamic similarity metrics/dynamic features constructed in this study. Nevertheless, RandomForest, the ensemble-based classifier, outperformed others in the case of undirected and co-authorship networks, whereas the linear classifier performs well in the directed network. In regard to the ensemble-based classifiers, bagging with a decision tree used as a base classifier is susceptible to overfitting and computationally expensive, as it considers all the available features to split a node in decision trees. Conversely, the RandomForest, a special case of bagging, randomly considers only a subset of the best features of those available. Therefore, it performed superior to bagging in some cases. In the co-authorship networks, we observed better performance by the bagging algorithm considering all three performance metrics. Considering AUCROC scores in the RandomForest classifier across the classification datasets, this study observed better performance in \(G_{MIT}\), and in the rest of the network datasets, scores are better in comparison to a random algorithm having the highest AUCROC score of 0.50. The highest AUCROC score is the RandomForest algorithm in \(G_{UCI}\), whereas, in co-authorship networks, the highest AUCROC score was achieved in \(G_{pl}\) by the same classifier. In regard to the AUCR scores, in all networks, we have observed that all classifiers surpassed the minimum score of 0.04. Although the RandomForest algorithm demonstrated improved performance in most cases using this metric, interestingly, it demonstrated inferior performance in regard to AUCR in \(G_{pl}\) dataset. The highest AUCR score achiever is the Bagging algorithm in \(G_{th}\). Considering the aforementioned discussion on classifiers' performances, we found that the RandomForest algorithm in the undirected networks, the linear classifier, logistic regression in the directed network, and Bagging in the co-authorship networks exceeded others in regard to performance metrics. Although a further study can reveal the underlying reason behind the classification performance differences demonstrated by different classifiers, it is evident from the classifiers' performances in this table, and we can conclude that the dynamic similarity metrics constructed in this study can be successfully utilized to predict future links in dynamic networks. Considering the Random Forest classifier, in \(G_{MIT}\), considering accuracy and AUCROC scores, although better performance was observed in the classification dataset constructed using a monthly sliding window (i.e., 30 days); however, considering the AUCR score, it is the window size of seven days that performs well. Likewise, the similar window size provided better performance in \(G_{Email}\) and \(G_{FF}\). However, in \(G_{UCI}\), we observed better performance in the classification datasets where the dynamic features were constructed using a window size of one day. On the other hand, in the directed network \(G_{Retur}\), considering the linear classifier, we have observed that the classification dataset, built upon considering hourly window size, has outperformed others. Based on the aforementioned observations, it is evident that the choice of the window resolution to sample or aggregate links in a dynamic network greatly impacts dynamic link prediction considering similarity/proximity metrics built upon the evolutionary information. This study took the advantage of three different algorithms provided in the WEKA machine learning software. In Table 4, we provide a comparable picture of these features in regard to their rank of importance obtained by these algorithms.