# A Scalable Deep Network for Graph Clustering
## 6 Ablation Study  
To further verify the effectiveness of our proposed model, we adopt two variant methods to verify the performance of each module. SDN\({}_{P}\) only employs the Quasi-GNN module and SDN\({}_{E}\) only utilizes the DNN module for encoding. Finally, we perform K-means on the embeddings obtained from them. Compared with other baselines, the experimental results show that removing either of the above two modules will lead to a decrease in accuracy and other metrics, which indicates that the two components of our proposed model are inseparable.  
Moreover, it is worth noticing that some results obtained by SDN\({}_{P}\) are better than baselines. For instance, the accuracy in **Reutuers**, **ACM**, and **CiteSeer** of SDN\({}_{P}\) is about **2%**, **0.5%**, and **1%** higher than that of AGCN. In addition, other metrics of SDN\({}_{P}\) also surpass AGCN with varying degrees, and this result indicates that the decoupled method we proposed still has superior performance while having high scalability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **USPS** & **HHAR** & **Reuters** & **ACM** & **CiteSeer** & **DBLP** \\ \hline K-means & 54.55 & 46.09 & 27.95 & 30.60 & 13.43 & 6.97 \\ AE & 58.83 & 60.36 & 49.55 & 54.64 & 29.31 & 12.21 \\ Random swap & 50.80 & 45.21 & 23.74 & 17.73 & 15.11 & 4.23 \\ K-algorithm & 35.71 & 24.46 & 11.17 & 0.76 & 7.97 & 7.97 \\ M-algorithm & 30.76 & 29.98 & 1.22 & 1.27 & 0.02 & 0.05 \\ \hline DEC & 63.70 & 61.25 & 48.44 & 60.64 & 28.12 & 23.92 \\ IDEC & 67.86 & 62.83 & 51.26 & 62.16 & 25.70 & 25.37 \\ GAE & 50.30 & 42.63 & 19.61 & 59.46 & 33.55 & 22.02 \\ VGAE & 40.96 & 51.47 & 26.18 & 57.72 & 33.13 & 17.92 \\ DAEGC & 63.33 & 60.38 & 31.12 & 59.35 & 37.78 & 21.03 \\ ARGA & 51.10 & 44.70 & 24.50 & 62.90 & 33.40 & 22.70 \\ SDCN & 71.84 & 72.84 & 55.36 & 73.91 & 40.17 & 39.15 \\ AGCN & **73.61** & 77.07 & 60.55 & 74.20 & 43.79 & 42.49 \\ \hline
**SDN\({}_{P}\)** & 71.26 & 72.78 & 62.05 & 75.39 & 44.07 & 27.71 \\
**SDN\({}_{E}\)** & 58.74 & 71.33 & 58.07 & 66.39 & 33.42 & 30.45 \\
**SDN** & 71.94 & **79.01** & **62.26** & **76.03** & **45.90** & **45.64** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average Rand Index (ARI) results on six datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **USPS** & **HHAR** & **Reuters** & **ACM** & **CiteSeer** & **DBLP** \\ \hline K-means & 64.78 & 58.33 & 41.28 & 67.57 & 36.08 & 31.92 \\ AE & 69.74 & 66.36 & 60.96 & 82.01 & 53.80 & 52.53 \\ Random swap & 60.78 & 58.22 & 46.34 & 58.68 & 29.98 & 34.88 \\ K-algorithm & 57.71 & 44.69 & 37.76 & 36.93 & 29.51 & 29.51 \\ M-algorithm & 51.88 & 28.24 & 21.66 & 30.26 & 6.55 & 11.69 \\ \hline DEC & 71.82 & 67.29 & 64.25 & 84.51 & 52.62 & 59.38 \\ IDEC & 74.63 & 68.63 & 63.21 & 85.11 & 61.62 & 61.33 \\ GAE & 61.84 & 62.64 & 43.53 & 84.65 & 57.36 & 61.41 \\ VGAE & 53.63 & 71.55 & 57.14 & 84.17 & 57.70 & 58.69 \\ DAEGC & 72.45 & 76.89 & 61.82 & 87.07 & 62.20 & 61.75 \\ ARGA & 66.10 & 61.10 & 51.10 & 86.10 & 54.80 & 61.80 \\ SDCN & 76.98 & 82.58 & 65.48 & 90.42 & **63.62** & 67.71 \\ AGCN & **77.61** & 88.00 & **66.16** & 90.58 & 62.37 & 72.80 \\ \hline
**SDN\({}_{P}\)** & 76.60 & 73.21 & 65.38 & 91.08 & 62.16 & 51.28 \\
**SDN\({}_{E}\)** & 69.62 & 80.18 & 63.30 & 87.38 & 56.50 & 64.97 \\
**SDN** & 77.02 & **89.39** & 66.03 & **91.39** & 61.48 & **74.60** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Macro-F1 score (F1) results on six datasets.