# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Accuracy and Speed  
#### 5.3.1 Precision and Recall  
Using each dataset, we evaluated the performance of Adaptive-DecayRank using the precision-recall metric. First, we computed the anomaly score for all graph snapshots  
Figure 5: AUC-ROC for DARPA dataset: A comparison of Adaptive-DecayRank (**0.90**), AnomRank (0.86), and DecayRank (0.83), showcasing the superior anomaly detection performance of Adaptive-DecayRank.  
Figure 6: Adaptive-DecayRank scalability analysis on DARPA, CTU-13, and RTM-30 datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Aomenology**} & \multicolumn{1}{c}{**DARPA**} & \multicolumn{1}{c}{**CTU-13**} & \multicolumn{1}{c}{**RTM-30**} \\ \hline  & \multicolumn{3}{c}{Adaptive-DecayRank} \\ \hline \(\log\)-30 & 1.000 & 1.000 & 1.000 \\ \(\log\)-100 & 0.980 & 1.000 & 1.000 \\ \(\log\)-100 & 0.980 & 1.000 & 1.000 \\ \(\log\)-200 & 0.880 & 1.000 & 1.000 \\ \(\log\)-200 & 0.503 & 0.000 & 0.000 \\ \(\log\)-200 & 0.520 & 0.544 & 0.528 \\ \hline \multicolumn{4}{c}{AnomRank} \\ \hline \(\log\)-30 & 1.000 & 1.000 & 1.000 \\ \(\log\)-300 & 0.950 & 0.980 & 0.840 \\ \(\log\)-300 & 0.500 & 0.640 & 0.520 \\ \(\log\)-400 & 0.510 & 0.505 & 0.210 \\ \(\log\)-500 & 0.474 & 0.244 & 0.348 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision of Top 50–500 anomalies of Adaptive-DecayRank vs. AnomRank across DARPA, CTU, and RTM datasets.  
in each dataset and calculated the top-k most anomalous snapshots (\(k=50,100,\ldots,800\)). As shown in Table 3, DARPA has 1463 snapshots, CTU-13 has 301 snapshots, and RTM has 101 snapshots.  
In Table 4, Adaptive-DecayRank achieves a high average precision score of **0.6455** on **DARPA**, compared to the baselines: AnomRank with 0.5665, DecayRank with 0.572, DynAnom with 0.58, and SedanSpot with 0.56. This represents a **13.94**% improvement over AnomRank, **12.86**% over DecayRank, **11.28**% over DynAnom, and **15.27**% over SedanSpot. Additionally, our model's total runtime is **35** s faster than both SedanSpot and DynAnom. However, AnomRank and DecayRank have runtimes of 4.03 and 5.46 s, respectively, which are faster than our method. Despite this, our model outperforms the baseline models in both precision and AUC, processing 1 million edges in 14.23 s (more details are provided in Section 5.4). This result is understandable given our iterative implementation of the decay factor with Bayesian updating at each time step.  
Likewise, on **CTU-13**, Adaptive-DecayRank achieves a high average precision of **0.6856** compared to AnomRank's 0.4118 and DecayRank's 0.6425. This represents a **66.49**% improvement over AnomRank and **6.71**% over DecayRank. On the **RTM-10** synthetic dataset (with 10% anomalies injected), Adaptive-DecayRank achieves **0.6746** compared to DecayRank's 0.5865 and AnomRank's 0.31. This represents a **117.61**% improvement over AnomRank and **15.02**% over DecayRank. Similarly, on the **RTM-30** dataset (i.e., with 30% anomalies injected), Adaptive-DecayRank achieves **0.6976** compared to AnomRank's 0.30 and DecayRank's 0.5699. This represents a **132.53**% improvement over AnomRank and **22.41**% over DecayRank. These results further demonstrate that our model is robust for detecting anomalies in large influx edge streams (due to algorithm and dataset compatibility constraints, we were unable to implement SedanSpot and DynAnom on the CTU-13 and RTM datasets.)