## 4 Results and Discussion
### Experimental Result
The CC-LT model, as depicted in Figure 17c, activates more nodes by focusing on local clustering, leading to more effective diffusion within localized regions. In contrast, the SH-LT model, as seen in Figure 17d, activates a larger number of nodes by leveraging the influence of structural hole users, which allows information to bridge disconnected communities. Figure 15: Propagation impact on the Wiki-Vote dataset. As shown in Figure 17e,f, the UPI-LT model consistently activates more nodes than the other models, with performance closely matching that of the PR-LT model but with a slight advantage in terms of the number of activated nodes. This demonstrates that by integrating multiple factors, the UPI-LT model can more effectively spread information across the network. When examining the spatial distribution of activated nodes, it becomes evident that models focusing on single factors, such as the local clustering coefficient (CC-LT) or PageRank (PR-LT), offer a limited view of the diffusion dynamics. The Wiki-Vote dataset, which lacks distinct community structures and exhibits weak clustering, challenges these models to produce meaningful diffusion patterns. This dataset's structural properties make it difficult to visualize clear diffusion trends. However, the UPI-LT model, which combines PageRank, structural holes, and local clustering, stands out as a more comprehensive approach for the modeling of information dissemination. It shows a more balanced distribution of activated nodes across the network, offering a deeper understanding of how different node attributes contribute to information spread. As seen in Figure 18, the UPI-LT model consistently outperforms other models on the Facebook dataset as well, activating more nodes across various seed node counts, ranging from \(k=5\) to \(k=35\). The number of activated nodes increases proportionally with the number of seed nodes for all models, but the UPI-LT model shows superior performance across the board. While the classic LT model demonstrates a stronger propagation effect on the Facebook dataset compared to Wiki-Vote, models that incorporate node-specific  
Figure 16: Runtime on the Wiki-Vote dataset. Figure 17: Visualization of propagation models on the Wiki-Vote dataset. attributes, such as SH-LT and CC-LT, perform even better. The SH-LT model highlights the importance of structural hole users, who bridge different communities and facilitate the spread of information. It confirms that nodes located at structural holes play a pivotal role in ensuring the flow of information between otherwise disconnected parts of the network. The PR-LT model also performs well in activating a large number of nodes, but as indicated by the runtime comparison shown in Figure 19, it requires more computational time than other models. In contrast, the UPI-LT model strikes the optimal balance between node activation and computational efficiency. Figure 19 shows that the UPI-LT model achieves the shortest runtime after the IC model, which does not account for node attributes. This suggests that the UPI-LT model is not only effective but also computationally efficient, making it a superior choice for large-scale networks. The CC-LT model also performs well in terms of runtime but fails to match the node activation level of the UPI-LT model. The SH-LT model, although effective in spreading information through structural holes, takes the most time to execute due to the computational complexity involved in identifying these nodes. This highlights the trade-off between activation effectiveness and runtime efficiency for models focusing on structural hole nodes. Figure 20 provides a visualization of the infection status of nodes in the Facebook dataset across different models. Inactive users are represented by gray nodes, while active (infected) users are shown in pink. The IC and LT models activate fewer nodes, with most  
Figure 19: Runtime on the Facebook dataset. Figure 18: Propagation impact on the Facebook dataset. of the network remaining gray.