# A Scalable Deep Network for Graph Clustering
## 4 Experiments
### Evaluation Metrics and Experimental Setup
#### 4.3.2 Experimental Setup  
To ensure the consistency of the experiments, we utilize a unified pre-train auto-encoder to train the benchmark models involving the DNN module, such as **AE+K-means**, **DEC**, **IDEC**, **SDCN**, and **AGCN**. The structure of the pre-train auto-encoder is a 4-layer encoder and a 4-layer decoder with the dimension of 500-2000-500-10, and the two components are completely symmetrical to ensure the consistency of the constructed features. Meanwhile, we adopt the learning rate of \(10^{-3}\) and 30 epochs to train the auto-encoder and restore the optimal training results. In the subsequent training, we first employ the pre-train auto-encoder to encode the data, then we perform the K-means and initialize our clustering layer with the obtained clustering results. During the training, different learning rates and epochs are used for different datasets. Table 3 shows the detailed settings for training the Quasi-GNN module in different datasets. For the \(\beta\) and \(\gamma\) in the loss function, we set them as \(\beta=10^{-1}\) and \(\gamma=10^{-2}\), respectively, in the experiment. Also, we set \(\alpha\) in the linear propagation layer to 0.3 and the degrees of freedom of the Student's t-distribution to 1. For the application of Random Swap on each dataset, we set the number of iterations to 10 and perform K-means twice for each iteration, the rest of the settings are the default settings; please refer to [[https://github.com/uef-machine-learning/RandomSwap](https://github.com/uef-machine-learning/RandomSwap)]([https://github.com/uef-machine-learning/RandomSwap](https://github.com/uef-machine-learning/RandomSwap)), accessed on 12 April 2022. Moreover, by applying the K-algorithm and M-algorithm, we need to reconstruct the data according to the corresponding input data format. Therefore, we obtain the corresponding neighbor nodes according to their adjacency matrix and measure their similarity according to the nodes' features like the weight of the edge. On this basis, when using the K-algorithm and the M-algorithm, we calculated the conductance as the cost function; in particular, for the M-algorithm, we set the number of iterations to 100. Other parameters are default; please refer to [[https://github.com/uef-machine-learning/gclu](https://github.com/uef-machine-learning/gclu)]([https://github.com/uef-machine-learning/gclu](https://github.com/uef-machine-learning/gclu)), accessed on 12 April 2022. On the other hand, K-means, Random Swap, and AE perform graph clustering directly on the feature matrix (vector data), K-algorithm and M-algorithm perform graph clustering on the data after reconstructing the input format, and other methods based on graph neural networks utilize a combination of feature matrix (vector data) and adjacency matrix (graph data). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ \hline Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix. For binary classification issues, the rows of the matrix represent the true values and the columns of the matrix represent the predicted values. **TP (True Positive)** means the number of positive samples predicted as positive samples, **FN (False Negative)** means the number of positive samples predicted as negative samples, **FP (False Positive)** means the number of negative samples predicted as positive samples, **TN (True Negative)** means the number of negative samples predicted as negative samples. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Learning Rate** & **Epochs** & **K** \\ \hline USPS & \(10^{-3}\) & 200 & 3 \\ HHAR & \(10^{-3}\) & 550 & 5 \\ Reuters & \(10^{-4}\) & 120 & 5 \\ ACM & \(10^{-3}\) & 120 & None \\ CiteSeer & \(10^{-3}\) & 120 & None \\ DBLP & \(10^{-3}\) & 120 & None \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter settings used for each training set when training the Quasi-GNN module.