# A Scalable Deep Network for Graph Clustering
## 5 Result Analysis  
We compare SDN with representative benchmark models and conduct extensive experiments on five datasets, including **HHAR**, **Reuters**, **ACM**, **CiteSeer**, and **DBLP**. The benchmark models completely adopt the original parameter settings. Moreover, the specific experimental results for different metrics are shown in Tables 5-8, where the **bold** values represent the best performance, the underlined values indicate the second-best performance. Our model surpasses recent benchmark models and achieves SOTA results. Compared with SDCN, our module has the following advantages:  
* We decouple the GCN module by employing the Quasi-GNN module to capture the information of graph topology and node features, and this module can be combined with methods such as smoothing or label propagation, which makes our model have high scalability. * We solve the issue of the orthogonal relationship between the model depth and the range of neighborhood, which enables the two to scale together and reduces the computational costs. * We simplify the model's structure and add the structural information to the Quasi-GNN module to alleviate the over-smoothing issue. Figure 2: Time consumption comparison chart of AGCN, SDCN, and SDN on six datasets. The experiments are conducted on a machine with Intel(R) Xeon(R) Gold 6130 CPU @ 2.10 GHz, and a single NVIDIA GeForce RTX 2080 Ti with 11 GB memory. The operating system of the machine is Ubuntu 18.04.5 LTS. As for software versions, we use Python 3.9.12, Pytorch 1.11.0 and CUDA 11.4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **USPS** & **HHAR** & **Reuters** & **ACM** & **CiteSeer** & **DBLP** \\ \hline K-means & 62.63 & 58.86 & 41.54 & 32.44 & 16.94 & 11.45 \\ AE & 67.53 & 71.42 & 49.69 & 49.30 & 27.64 & 25.40 \\ Random swap & 60.51 & 58.86 & 29.06 & 16.40 & 17.26 & 11.24 \\ K-algorithm & 50.70 & 31.57 & 11.88 & 0.88 & 7.69 & 7.69 \\ M-algorithm & 57.54 & 50.60 & 4.03 & 1.27 & 0.71 & 0.38 \\ \hline DEC & 70.58 & 72.91 & 47.50 & 54.54 & 28.34 & 29.51 \\ IDEC & 75.56 & 74.19 & 50.28 & 56.61 & 27.17 & 31.17 \\ GAE & 60.69 & 55.06 & 25.92 & 55.38 & 34.63 & 30.80 \\ VGAE & 51.08 & 62.95 & 25.51 & 53.20 & 32.69 & 26.92 \\ DAEGC & 71.12 & 69.10 & 30.55 & 56.18 & 36.41 & 32.49 \\ ARGA & 61.60 & 57.10 & 28.70 & 55.70 & 34.50 & 26.80 \\ SDCN & 79.51 & 79.90 & 50.82 & 68.31 & 38.71 & 39.50 \\ AGCN & **79.64** & 82.44 & 57.83 & 68.38 & 41.54 & 39.68 \\ \hline
**SDN\({}_{P}\)** & 78.79 & 82.31 & **59.52** & 69.70 & 41.95 & 27.86 \\
**SDN\({}_{E}\)** & 67.51 & 78.96 & 56.35 & 59.78 & 32.84 & 31.07 \\
**SDN** & **79.64** & **83.02** & 59.49 & **70.35** & **44.28** & **41.84** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normalized Mutual Information (NMI) results on six datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **USPS** & **HHAR** & **Reuters** & **ACM** & **CiteSeer** & **DBLP** & **References** \\ \hline K-means & 66.82 & 59.98 & 54.04 & 67.31 & 39.32 & 38.65 & [32] \\ AE & 71.04 & 68.69 & 74.90 & 81.83 & 57.08 & 51.43 & [22] \\ Random swap & 63.47 & 59.21 & 58.92 & 59.70 & 38.62 & 38.75 & [33] \\ K-algorithm & 56.27 & 45.06 & 44.24 & 37.82 & 32.40 & 32.44 & [21] \\ M-algorithm & 52.98 & 42.31 & 44.25 & 38.08 & 21.34 & 29.60 & [21] \\ \hline DEC & 73.31 & 69.39 & 73.58 & 84.33 & 55.89 & 58.16 & [34] \\ IDEC & 76.22 & 71.05 & 75.43 & 85.12 & 60.49 & 60.31 & [35] \\ GAE & 63.10 & 62.33 & 54.40 & 84.52 & 61.35 & 61.21 & [5] \\ VGAE & 56.19 & 71.30 & 60.85 & 84.13 & 60.97 & 58.59 & [5] \\ DAEGC & 73.55 & 76.51 & 65.50 & 86.94 & 64.54 & 62.05 & [9] \\ ARGA & 66.80 & 63.30 & 56.20 & 86.10 & 56.90 & 61.60 & [7] \\ SDCN & 77.89 & 84.26 & 77.15 & 90.45 & 65.96 & 68.05 & [10] \\ AGCN & **80.98** & 88.11 & 79.30 & 90.59 & 68.79 & 73.26 & [36] \\ \hline
**SDN\({}_{P}\)** & 77.66 & 78.61 & 81.07 & 91.07 & 69.52 & 60.56 & Proposed \\
**SDN\({}_{E}\)** & 70.96 & 82.11 & 79.47 & 87.44 & 60.14 & 65.98 & Proposed \\
**SDN** & 78.04 & **89.50** & **81.15** & **91.34** & **70.78** & **74.93** & Proposed \\ \hline \hline \end{tabular}
\end{table}