# Outlier Detection and Prediction in Evolving Communities
## 7 Conclusions  
In this paper, we have proposed an online method for detecting communities and outlier nodes in time-evolving social networks where the structure and content of the network are both taken into account. The main idea behind our approach is to perform community detection in parallel to the assignment of an outlier score to each node that depicts its abnormality in terms of both structure and content. The outlier score is a degree of a node's outlier behavior, with higher values representing nodes that are less connected to the graph and have content that is differentiated from their neighbors. Our approach is adaptive, as users can set an appropriate context-dependent threshold for a node's \begin{table}
\begin{tabular}{l l c c} \hline \hline  & & **Betweenness Centrality + PageRank** & **Outlier Score** \\ \hline Chain length: 3 & Accuracy & 0.6129 & **0.7234** \\  & F-score & 0.4946 & **0.6965** \\  & Avg Precision & “TRUE” & 0.16 & **0.48** \\  & “FALSE” & **0.92** & 0.89 \\  & Avg Recall & “TRUE” & 0.57 & **0.77** \\  & “FALSE” & 0.62 & **0.71** \\ \hline Chain Length: 4 & Accuracy & 0.8056 & **0.9167** \\  & F-score & 0.8017 & **0.9150** \\  & Avg Precision & “TRUE” & 0.80 & **0.93** \\  & “FALSE” & 0.81 & **0.90** \\  & Avg Recall & “TRUE” & 0.75 & **0.88** \\  & “FALSE” & 0.85 & **0.95** \\ \hline \hline \end{tabular}
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Table 10: Chain classification results: MovieLens. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Classifier** & **Structure Score** & **Betweenness Centrality** & **PageRank** & **Degree** & **Centrality + Rank + Degree** \\ \hline StackExchange & & & & & & \\ \hline  & SVC & 0.5862 & 0.6872 & 0.7097 & **0.7308** & **0.7308** \\  & kNNS & 0.7094 & 0.6583 & 0.6917 & 0.7166 & **0.7352** \\  & DT & 0.6388 & 0.6872 & 0.7097 & **0.7379** & 0.7205 \\ \hline  & & & & & & \\ \hline  & SVC & **0.7779** & 0.7454 & 0.7454 & 0.7419 & 0.7419 \\  & kNNS & **0.7939** & 0.7712 & **0.7939** & 0.7731 & 0.7607 \\  & DT & 0.7565 & 0.7844 & **0.7948** & 0.7589 & 0.7744 \\ \hline MovieLens & & & & & & \\ \hline  & SVC & 0.6231 & 0.6266 & 0.7177 & **0.7863** & **0.7863** \\  & kNNS & 0.6916 & 0.6266 & 0.6694 & 0.7562 & **0.7821** \\  & DT & 0.6803 & 0.6215 & 0.7056 & 0.7863 & 0.7738 \\ \hline  & & & & & & \\ \hline  & SVC & **0.8701** & 0.8149 & 0.8149 & 0.8508 & 0.8508 \\  & kNNS & **0.8427** & 0.8335 & 0.8297 & 0.8347 & 0.8105 \\  & DT & **0.8566** & 0.8303 & 0.8177 & 0.8056 & 0.8245 \\ \hline \hline \end{tabular}
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Table 8: Classification results. \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & & **Betweenness Centrality + PageRank** & **Outlier Score** \\ \hline Chain length: 3 & Accuracy & 0.7054 & **0.7455** \\  & F-score & **0.6735** & 0.6627 \\  & Avg Precision & “TRUE” & **0.68** & 0.43 \\  & “FALSE” & 0.72 & **0.87** \\  & “TRUE” & 0.49 & **0.58** \\  & “FALSE” & 0.68 & **0.79** \\ \hline Chain Length: 4 & Accuracy & 0.7246 & **0.7591** \\  & F-score & 0.6935 & **0.7065** \\  & Avg Precision & “TRUE” & **0.78** & 0.55 \\  & “FALSE” & 0.71 & **0.85** \\  & “TRUE” & 0.48 & **0.62** \\  & “FALSE” & **0.90** & 0.81 \\ \hline \hline \end{tabular}
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Table 9: Chain classification results: StackExchange. minimum outlier score to be considered as an outlier. The user can leverage the importance assigned to each dimension in both community and outlier detection procedures through the use of appropriate weights; thus, the weight of content characteristics can be lowered when structural connections are assessed as more important, and vice versa. To validate the usability of the proposed outlier score, we conducted an extensive experimental study based on two real world datasets with different characteristics in terms of structure and content. Our results reveal that the proposed outlier score can successfully serve a twofold purpose.