# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 5 Conclusions  
This paper introduces an enhanced autoencoder-based framework for graph anomaly detection, leveraging the subgraph information to improve the detection performance. In the subgraph preprocessing stage, the model extracts k-hop subgraphs around each node in the graph, aggregating local topological information to generate enhanced node representations. Additionally, the extracted local topological information is leveraged to provide an extra node-subgraph perspective, strengthening the model's understanding of the graph structure. In the decoding stage, a graph structure learning method is employed to strengthen the structure reconstruction capability. Furthermore, a neighborhood selection step is incorporated into the anomaly scoring step to improve anomaly detection results after training. Experimental evaluations validated the superiority of the proposed method in terms of detection accuracy compared to state-of-the-art baselines. The proposed method outperformed the baseline models across five out of six datasets overall. When compared with other graph autoencoder-based methods, the proposed model achieved an average improvement of 4.09% in the AUC score over all six datasets. In comparison to graph contrastive learning-based frameworks, the proposed model shows an improvement of 5.11% in AUC scores across five datasets and falls behind in only one dataset, by a small margin. Through further ablation studies, the effectiveness of each component within the proposed framework in contributing to the model's detection performance was also verified and highlighted. While the proposed model demonstrates significant advancements in graph anomaly detection tasks, several directions remain unexplored for future research. The current design of the framework primarily addresses static graphs for local subgraph information extraction and aggregation. Future work could investigate the extension of the subgraph preprocessing stage to dynamic graphs or evolving networks, where the graph structure undergoes changes over time. Exploiting the possibility of incorporating temporal information into the subgraph preprocessing process remains to be studied. Moreover, real world graphs can be heterogeneous, implying that nodes in the graph can be connected through various relation types. Exploring how a graph-autoencoder-based model can effectively learn interrelations between different connection types to \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variant Dataset** & **Cora** & **CiteSeer** & **Pubmed** & **BlogCatalog** & **Flickr** & **ACM** \\ \hline w/o e.e. & 0.933 \(\pm\) 0.003 & 0.954 \(\pm\) 0.001 & 0.943 \(\pm\) 0.002 & 0.806 \(\pm\) 0.003 & 0.797 \(\pm\) 0.001 & 0.818 \(\pm\) 0.004 \\ \hline w/o e.e. and e.v. & 0.931 \(\pm\) 0.002 & 0.950 \(\pm\) 0.001 & 0.943 \(\pm\) 0.001 & 0.807 \(\pm\) 0.001 & 0.798 \(\pm\) 0.001 & 0.797 \(\pm\) 0.010 \\ \hline w/o gsl & 0.937 \(\pm\) 0.002 & 0.977 \(\pm\) 0.000 & 0.947 \(\pm\) 0.001 & 0.826 \(\pm\) 0.001 & 0.805 \(\pm\) 0.001 & 0.923 \(\pm\) 0.003 \\ \hline w/o n.s. & 0.934 \(\pm\) 0.001 & 0.961 \(\pm\) 0.001 & 0.941 \(\pm\) 0.001 & 0.827 \(\pm\) 0.000 & 0.802 \(\pm\) 0.001 & 0.923 \(\pm\) 0.000 \\ \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 5: Performance comparison with different variants. generate latent embeddings and perform heterogeneous graph anomaly detection poses a challenging, yet promising direction for future work. Conceptualization, [PERSON]; methodology, C.Z. ; validation, C.Z. and [PERSON]-[PERSON]; formal analysis, C.Z. ; investigation, [PERSON]; resources, C.Z. and [PERSON]-W.J. ; data curation, C.Z. ; writing--original draft, C.Z. ; writing--review and editing, [PERSON] and [PERSON]-W.J. ; visualization, [PERSON] and [PERSON]-[PERSON]; supervision, J.-W.J. ; project administration, J.-W.J. ; funding acquisition, J.-W.J.