## 4 Results and Discussion
### Experimental Result  
The experimental results demonstrate the effectiveness of the proposed UPI-LT model compared to several baseline models in terms of information propagation. As illustrated in Figure 15, an increase in the number of seed nodes leads to a corresponding rise in the number of activated (infected) nodes across all models. However, the UPI-LT model consistently outperforms others, showing a broader spread of information. This highlights the significance of considering multiple node attributes in enhancing the diffusion process. When comparing different models, it is evident that the classic LT and IC models activate the fewest nodes. This is primarily because these models rely on randomly assigned activation thresholds, leading to suboptimal propagation results. In contrast, models that incorporate specific node attributes, such as the SH-LT and PR-LT models, demonstrate significantly better performance. The SH-LT model, which emphasizes the role of structural hole nodes, activates more nodes than the CC-LT model, which only focuses on local clustering coefficients. This indicates the critical role that structural hole nodes play in expanding \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **PageRank** & **Clustering Coefficient** & **Constraint** & **Time Decay** & **Homogeneity** \\ \hline IC & & & & & \\ LT & & & & \\ PR-LT & & & & \\ SH-LT & & & & \\ CC-LT & & & & \\ UPI-LT & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptions of five models of information dissemination. Notation ’\(\check{\mathbf{\psi}}\)’ is used to indicate that a model includes the corresponding feature, and ’\(\check{\mathbf{\chi}}\)’ to indicate that the feature is not included in the model. information dissemination, as their unique positions across multiple communities allow them to act as key conduits for information flow. Moreover, the PR-LT model, which prioritizes nodes based on their PageRank scores, achieves a performance level comparable to that of the UPI-LT model in terms of the number of activated nodes. However, the UPI-LT model exhibits a clear advantage in computational efficiency. As shown in Figure 16, when the number of seed nodes is set to \(k=30\), the UPI-LT model requires significantly less runtime than the PR-LT model while still activating a comparable number of nodes. This demonstrates that the UPI-LT model not only excels in terms of the number of nodes it activates but also in maintaining a lower computational cost, making it particularly suitable for large-scale networks. The experimental results also highlight the differences in time complexity across models. The LT model takes the longest time to run, likely due to its inefficiency in handling the random assignment of activation thresholds. In contrast, the UPI-LT model achieves the shortest runtime, followed closely by the CC-LT model. This demonstrates that the UPI-LT model provides an ideal balance between computational efficiency and activation effectiveness, making it a highly efficient model for large networks. The experimental results also highlight the differences in time complexity across models. The LT model takes the longest time to run, likely due to its inefficiency in handling the random assignment of activation thresholds. In contrast, the UPI-LT model achieves the shortest runtime, followed closely by the CC-LT model. This demonstrates that the UPI-LT model provides an ideal balance between computational efficiency and activation effectiveness, making it a highly efficient model for large networks. Figure 17 visualizes the activation patterns of different models on the Wiki-Vote dataset. Light-green nodes represent inactive nodes, while pink nodes represent activated nodes. As seen in Figure 17a, the IC model activates the fewest nodes, resulting in a sparse spread of information. The LT model performs slightly better, as shown in Figure 17b, but it still falls short compared to models that consider node attributes.