# Outlier Detection and Prediction in Evolving Communities
## 6 Predicting Outlying Behavior of Nodes
### Exploration of Outlier Scores and Future Behavior
The scores in bold are the highest observed in each case. For both datasets, it can be observed that when only structural features are exploited, the node degree and the combination of all three popular structural measures achieve the highest scores at up to 73.79% for StackExchange and up to 78.63% for MovieLens. However, when the content part is included, our outlier score is superior to all others, outperforming all of them in five of six cases. In addition, it achieves higher performance overall, with an F-score of up to 79.48% for StackExchange and up to 85.86% for MovieLens, which is a relative improvement of more than 7% for StackExchange and 10% for MovieLens. Further focusing on time evolution, we investigated whether exploiting the history of nodes in the past can contribute to improved performance on our classification problem. We modeled a node's history by collecting its feature values at a series of time points during which the node survives, and used these as the input to our classifiers. To this end, we only focused on nodes that survived for at least three time slices and did not turn inactive (i.e., leave the network) within this period. We measured the features of each node in each time slice, constructing _chains_ of such features. Recall that the \(ttl\) and \(obs\) pair selected is 60/30; thus, most nodes are investigated during two time slices, and unless their lifetime is refreshed they stay inactive and are not considered. Tables 9 to 10 show the results achieved in accuracy, F-score, and more specifically precision and recall measures per class, respectively, for the nodes whose past characteristics were taken into account for our two datasets. We compared the outlier score with betweenness centrality combined with PageRank. We selected the Support Vector Classifier as our classifier, as in the previous experiments it showed better results and as, in contrast to \(k-NN\), it is not parametric. We experimented with chain lengths of 3 and 4, expecting the higher chain length to provide more accurate results, as it incorporates additional information. The distribution between the two classes in the StackExchange dataset contained 32.09% of the node members in communities (TRUE), while on the MovieLens dataset the rate was 59.52%. Focusing on the results, where the highest scores are depicted in bold, it can be seen that using our outlier score as input can result in better overall accuracy scores as well as better per-class precision and recall. As expected, using a chain length of 4 resulted in better accuracy. Comparing the results with the previous set of experiments, where only one time slice of each node was taken into account, it can be concluded that using a chain of consecutive node observations can result in improved observations. as we achieved an F-score of up to 91.5% for chains of length 4. Notably, all metrics in this case were 88% or above, without any class underperforming in either precision nor recall, as can occasionally be observed with chains of smaller length.