## 5 Conclusions  
This paper introduces a novel information propagation model, the User Propagation Influence Linear Threshold (UPI-LT) model, which advances the understanding of information dissemination by incorporating multiple node attributes and social factors. In contrast to traditional models, which typically rely on a single metric to assess user influence, the UPI-LT model integrates a comprehensive set of factors, including global, local, and individual node characteristics. The UPI-LT model employs metrics such as PageRank, the local clustering coefficient, and the network constraint coefficient to provide a more nuanced and accurate representation of the influence of network topology on information spread. A significant methodological contribution of the UPI-LT model is its capacity to account for both topological and social factors affecting information propagation in a dynamic manner. The model incorporates the homophily ratio to adjust a node's activation threshold, thereby acknowledging the importance of similarity between connected users. Furthermore, the time decay of influence is factored into the model, reflecting the real-world phenomenon whereby a user's impact on others diminishes over time. This dynamic consideration transforms the traditionally static approach of influence accumulation into a more realistic, temporally aware process. Experimental results on real-world datasets validate the effectiveness of the UPI-LT model. The findings demonstrate that the model consistently activates more nodes and achieves broader information dissemination than baseline models. The superior performance of UPI-LT confirms that incorporating both network structure and temporal dynamics is critical for accurately simulating and enhancing information spread. In addressing the research questions posed at the outset, this study has demonstrated that the UPI-LT model effectively identifies and quantifies the influence of key users within evolving social networks. By integrating homophily, time decay, and dynamic thresholds, the model provides a more nuanced understanding of how influence propagates, especially in heterogeneous and dynamic environments. Furthermore, the combination of multiple influence factors, namely PageRank, structural holes, and local clustering, results in significant improvements in the breadth and efficiency of information dissemination, answering the research questions regarding the benefits of a multifaceted approach. The implications of this research extend beyond the scope of influence modeling, suggesting potential applications in areas such as rumor control, targeted marketing, and emergency information dissemination. However, there are limitations to the current study. One major limitation is that the model's effectiveness is highly dependent on the accuracy of the input data, particularly regarding user attributes and network dynamics. Additionally, while the model accounts for temporal dynamics, it does not explicitly address the impact of external factors, such as sudden changes in user behavior or platform-specific interventions, which could significantly influence propagation outcomes. Future research should focus on several areas to address these limitations. First, enhancing the model to incorporate external events or shocks, such as news events or policy changes, could provide a more comprehensive understanding of influence dynamics. Additionally, extending the model to support multi-layered networks, where users participate in multiple overlapping social contexts, could further improve the accuracy of influence propagation predictions. Finally, exploring adaptive learning mechanisms that allow the model to continuously refine its parameters based on real-time data could make the UPI-LT model more robust in rapidly changing environments. Conceptualization, [PERSON] and X.G. ; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and X.G.