# Outlier Detection and Prediction in Evolving Communities
## 5 Evaluation Results
### Parameter Tuning  
Two of the main inputs in the algorithm are the \(obs\) and \(ttl\) values, respectively standing for the observation window period and time-to-live of each edge and its labels. Both parameters are context-dependent. The flow of the algorithm is not affected by differentiating the observation window period; however, the user will be aware of more or less information regarding the graph's formations over time, that is, \(obs\) determines the granularity of the evolution monitoring. Higher \(obs\) values result in fewer algorithm outputs, and consequently in a speedup in execution time, while intermediate changes describing the graph's evolution are not recorded. Changing the \(ttl\) value, on the other hand, results in quite different behavior and intermediate outcomes, as certain connections may appear and disappear during the same observation window period if their lifetime is set too low. The \(ttl\) can be set to \(+\infty\) in an accumulation growth scenario; however, this results in challenges around memory \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Edges** & **Nodes** & **Labels** &
\begin{tabular}{c} **Timespan** \\ **(Years)** \\ \end{tabular} \\ \hline Stack Exchange & 542,120 & 87,438 & 2615 & 10 \\ MovieLens & 111,621 & 6113 & 1043 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset information. and computational costs. In general, high \(ttl\) values are appropriate for networks describing more permanent relationships between the participating entities, such as friendships in social networks, while low \(ttl\) values better fit networks with transient interactions, e.g., communication networks. Both \(obs\) and \(ttl\) are related, and setting high values of both is appropriate when the network is more stable and the analyst is not interested in monitoring all fluctuations, while low values enable more changes to be captured. In order to study these two parameters appropriately and configure them for our two datasets, we experimented with various pairs of values to observe the difference they would depict in the results. The time spans of the two datasets are quite similar, as shown in Table 4; thus, we experimented with similar value pairs. Table 5 shows the total number of detected communities along with the average number of community members and labels when different value pairs were selected. In order to record all edges at least one time, we only studied pairs where \(ttl\) was higher than \(obs\). Observing the table, the differences between the two datasets are obvious; StackExchange tends to form more communities but is smaller in terms of cardinality with less varying content, while MovieLens users tend to form a smaller number of larger groups. We selected the 60/30 pair of \(ttl\)/\(obs\) parameters for our experiments, as it derives a higher number of communities with more members for both datasets compared to pairs with lower \(ttl\), while setting \(ttl\) higher leads to insignificant differences. The weight value (\(w\)) is responsible for balancing the structure and content part of the outlier score. If it is set to 0, outliers are characterized only by their content score, while when it is set to 1 only the structural score of each node matters. Thus, the weight value can help in obtaining a context-dependent threshold reflecting the relative importance that the user wishes to assign to structure or content. Figure 2 depicts the distribution of outlier scores among the two datasets' nodes for different values of \(w\). The green line refers to the percentage of nodes with an outlier score over 0.50 (High OS), while the blue line refers to the percentage of nodes with an outlier score below or equal to 0.50 (Low OS). When the weight is balanced between structure and content (\(w=\frac{1}{2}\)), the detection of nodes with outlier scores higher than 0.50 is relatively low for StackExchange, at 45.95%, and particularly low for MovieLens at 24.29%.