# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 4 Experiments
### Parameter Sensitivity
#### 4.5.1 Effect of Balancing Parameter \(\alpha\)  
We vary the value of parameter \(\alpha\) from 0 to 1 using the set {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 1} to explore its impact on model performance. Specifically, \(\alpha=0\) corresponds to using only structure reconstruction error, while \(\alpha=1\) indicates exclusive reliance on attribute reconstruction error. As illustrated in Figure 2, across all datasets, relying solely on structure reconstruction error results in nearly the lowest AUC and F1 scores, whereas emphasizing attribute reconstruction error only yields better results. For the Cora and CiteSeer datasets, the model achieves peak performance when the \(\alpha\) value is around 0.6, suggesting a more balanced contribution of both errors. For the BlogCatalog, Flickr, Pubmed, and ACM datasets, the highest scores are attained when \(\alpha\) is in the 0.95 to 0.99 range. The results show that optimal detection performance is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ModelDataset** & **Cora** & **CiteSeer** & **Pubmed** & **BlogCatalog** & **Flickr** & **ACM** \\ \hline DOMINANT & 0.692 \(\pm\) 0.002 & 0.684 \(\pm\) 0.010 & 0.614 \(\pm\) 0.015 & 0.624 \(\pm\) 0.005 & 0.709 \(\pm\) 0.002 & 0.699 \(\pm\) 0.001 \\ \hline CoLA & 0.694 \(\pm\) 0.013 & 0.627 \(\pm\) 0.011 & 0.630 \(\pm\) 0.008 & 0.589 \(\pm\) 0.010 & 0.690 \(\pm\) 0.009 & 0.667 \(\pm\) 0.003 \\ \hline ComGA & 0.775 \(\pm\) 0.005 & 0.802 \(\pm\) 0.006 & 0.635 \(\pm\) 0.002 & 0.620 \(\pm\) 0.005 & 0.700 \(\pm\) 0.004 & 0.680 \(\pm\) 0.005 \\ \hline ANEMONE & 0.779 \(\pm\) 0.011 & 0.812 \(\pm\) 0.002 & 0.622 \(\pm\) 0.008 & 0.613 \(\pm\) 0.002 & 0.681 \(\pm\) 0.002 & 0.650 \(\pm\) 0.004 \\ \hline GCAD & 0.796 \(\pm\) 0.009 & 0.795 \(\pm\) 0.004 & 0.650 \(\pm\) 0.006 & 0.630 \(\pm\) 0.006 & 0.635 \(\pm\) 0.004 & 0.629 \(\pm\) 0.010 \\ \hline Our Model & **0.831 \(\pm\) 0.002** & **0.833 \(\pm\) 0.006** & **0.693 \(\pm\) 0.002** & **0.644 \(\pm\) 0.000** & **0.726 \(\pm\) 0.001** & **0.747 \(\pm\) 0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of F1 scores over six datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ModelDataset** & **Cora** & **CiteSeer** & **Pubmed** & **BlogCatalog** & **Flickr** & **ACM** \\ \hline DOMINANT & 0.893 \(\pm\) 0.004 & 0.879 \(\pm\) 0.005 & 0.872 \(\pm\) 0.011 & 0.781 \(\pm\) 0.001 & 0.751 \(\pm\) 0.001 & 0.778 \(\pm\) 0.006 \\ \hline CoLA & 0.903 \(\pm\) 0.008 & 0.828 \(\pm\) 0.006 & 0.929 \(\pm\) 0.014 & 0.754 \(\pm\) 0.003 & 0.735 \(\pm\) 0.002 & 0.833 \(\pm\) 0.010 \\ \hline ComGA & 0.889 \(\pm\) 0.004 & 0.911 \(\pm\) 0.010 & 0.921 \(\pm\) 0.001 & 0.812 \(\pm\) 0.003 & 0.792 \(\pm\) 0.005 & 0.850 \(\pm\) 0.004 \\ \hline ANEMONE & 0.917 \(\pm\) 0.011 & 0.925 \(\pm\) 0.004 & **0.952 \(\pm\) 0.001** & 0.808 \(\pm\) 0.002 & 0.762 \(\pm\) 0.001 & 0.859 \(\pm\) 0.005 \\ \hline GCAD & 0.906 \(\pm\) 0.003 & 0.929 \(\pm\) 0.004 & 0.920 \(\pm\) 0.002 & 0.761 \(\pm\) 0.005 & 0.747 \(\pm\) 0.003 & 0.846 \(\pm\) 0.002 \\ \hline Our Model & **0.942 \(\pm\) 0.003** & **0.979 \(\pm\) 0.001** & 0.950 \(\pm\) 0.001 & **0.828 \(\pm\) 0.001** & **0.806 \(\pm\) 0.001** & **0.924 \(\pm\) 0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of AUC scores over six datasets.  
achieved when both reconstruction errors are taken into consideration. Moreover, based on the observation, the attribute reconstruction errors appear to carry more weight than the structural reconstruction errors in our proposed model, since the subgraph embedding stage already injects structural information into the enhanced node attributes.