# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 5 Conclusions and Future Research  
In the present paper, a spectral clustering model based on deep neural networks named \(SEG\_SC\) is proposed. First, the model utilizes a sparse similarity graph based  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Blogdata** & **20 Newgroups** & **Seimic** & **Covtype** \\ \hline Graphencoder & 0.12 & 0.14 & 0.15 & 0.07 \\ PCA & 0.13 & 0.14 & 0.16 & 0.07 \\ SC & 0.13 & 0.16 & 0.18 & 0.08 \\ SEG\_SC & 0.14 & 0.16 & 0.17 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cluster comparison of NMI results in large-scale data.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Parameters** & **Range** & **Setpoints** & **Wine** & **P** & 200, 300, 400, 500, 600, 700, 800, 1000, 1200 & 1000 \\ g & sigmoid function, tanh function & sigmoid function & sigmoid function \\ m & 1, 2, 3, 4, 5 & 3 or 5 & 3 or 5 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameter table.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Wine** & **Pendigit** & **Mnist** & **Blogdata** & **20 Newgroups** & **Seimic** & **Covtype** \\ \hline PCA & 0.73 & 0.54 & 0.50 & 0.59 & 0.59 & 0.43 & 0.2 \\ SC & 0.81 & 0.62 & 0.62 & 0.58 & 0.6 & 0.5 & 0.27 \\ Graphencoder & 0.84 & 0.6 & 0.38 & 0.59 & 0.51 & 0.47 & 0.23 \\ DCN & 0.83 & 0.63 & 0.83 & 0.64 & 0.44 & 0.65 & 0.21 \\ SEG\_SC & 0.86 & 0.65 & 0.6 & 0.65 & 0.62 & 0.54 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(SEG\_SC\) experimental time.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Wine** & **Pendigit** & **Mnist** & **Blogdata** & **20 Newgroups** & **Seimic** & **Covtype** \\ \hline PCA & 0.73 & 0.54 & 0.50 & 0.59 & 0.59 & 0.43 & 0.2 \\ SC & 0.81 & 0.62 & 0.62 & 0.58 & 0.6 & 0.5 & 0.27 \\ Graphencoder & 0.84 & 0.6 & 0.38 & 0.59 & 0.51 & 0.47 & 0.23 \\ DCN & 0.83 & 0.63 & 0.83 & 0.64 & 0.44 & 0.65 & 0.21 \\ SEG\_SC & 0.86 & 0.65 & 0.6 & 0.65 & 0.62 & 0.54 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cluster comparison of ACC results.  
on landmark points and the KNN method, introducing a deep graph representation that encodes each vertex into a low-dimensional vector representation. Secondly, the model theoretically demonstrates the effectiveness of \(SEG\_SC\). Experiments involving the use of real data demonstrate that spectral clustering using deep learning can enhance clustering performance and reduce time consumption in various types of large-scale datasets of information networks.  
In future research, we plan to extend this model from general information networks to multi-dimensional information networks, enhancing its scalability on large-scale datasets. In addition, we plan to strengthen its impact by including case studies or real-world applications where the proposed method has been implemented. Conducting such studies will demonstrate its practical viability.  
L.N.: Conceptualization, methodology, software, investigation, formal analysis, writing--original draft preparation, writing--review and editing, funding acquisition; P.M.: visualization, validation, data curation, supervision, funding acquisition; W.Q.: investigation, formal analysis, resources. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China (Grant 2017 YFB0202901, 2017 YFB0202905), the Hunan Natural Science Foundation Project (No. 2023 JJ50353).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.