## 5 Experimental Results and Discussion
### Experiments on Microblog Network
Through the above steps, the space vector model of user interest was obtained. The SSC was used then to group Microblog users. The main process of SSC clustering was as follows: (1) The similarity matrix was calculated, and the coordinates in each space were normalized. The coordinate range was between [-1, 1]. (2) Sparse decomposition was obtained by using the MATLAB convex optimization toolkit to obtain the sparse coefficient matrix. The absolute value was obtained for each element of the sparse coefficient matrix, and then was normalized to construct an adjacency matrix. (3) The Laplacian matrix was obtained according to the adjacency matrix, and the singular values arranged in descending order were obtained by singular value decomposition. The corresponding right singular vector took \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sense** & **Nephew** & **Families** & **Mother** & **Brother** & **Father-in-Law** & **Mother-in-Law** \\
**Coding** & **Ah16A01** =** & **Ah01B01** =** & **Ah04B01** =** & **Ah09B01** =** & **Ah07A03** =** & **Ah02C01** =** \\ \hline Nephew & 1.00 & 0.14 & 0.27 & 0.50 & 0.41 & 0.18 \\ Families & 0.14 & 1.00 & 0.68 & 0.45 & 0.54 & 0.77 \\ Mother & 0.27 & 0.68 & 1.00 & 0.59 & 0.68 & 0.72 \\ Brother & 0.50 & 0.45 & 0.59 & 1.00 & 0.72 & 0.50 \\ Father-in-law & 0.41 & 0.54 & 0.68 & 0.72 & 1.00 & 0.59 \\ Mother-in-law & 0.18 & 0.77 & 0.72 & 0.50 & 0.59 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Similarity of partial feature words. the smallest n column to form a matrix, and the row vectors of the matrix were regularized. (4) The regularized matrix was clustered to get the user's group label. The user grouping situation and the corresponding key feature words are displayed in a visibility graph. Experimental results show that the number of clusters had a great influence on the clustering algorithm. The number of clusters by the number index was determined. The number was an evaluation index of intra-cluster density and inter-cluster dispersion. Its value was within the range of [-1, 1]. With the larger value of the number index, the higher the similarity of intra-cluster samples was, and the lower the similarity of inter-cluster samples was [33]. The average number of all data points was the total number of clustering results. The number of clusters was generally 2-10. With larger average contour coefficient, the clustering effect was better. The average value of the clustering results of different cluster numbers was calculated. When the number of clusters was 2, the average value was the largest, but the sum of squared errors was not the smallest. When the number of clusters was 3, the average contour coefficient was high while satisfying the error square sum as small as possible. The relationship between the contour coefficient and the number of clusters is shown in Figure 3. In the SSC, the smallest three vectors in the column represent the data points, and the clustering result is shown in Figure 4.  
Figure 3: Relation graph of SSC number and cluster number. For the related influencer accounts, users in the same cluster recommended each other's following list. At the same time, word clouds were generated from the text information of users in each cluster by word cloud tools. The degree of attention and participation of different user groups on products were obtained. As shown in Figure 5, the three groups of users had a high degree of attention for Xiaomi products and were actively involved in the polling activities initiated by Xiaomi companies. The first group of users paid much attention to the product performance and Xiaomi robot. The second group of users repeatedly mentioned the appearance and the motherboard of the mobile phone, the scratches appearing in the feedback. The third group of users was concerned about the price and quality problems of the products. They were opposed to the \"hunger marketing\" of the company. Therefore, different marketing strategies can be formulated and implemented according to the user groups with different concerns.