# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 6 Discussion  
### Comparison with Existing Models  
Our research findings both support and extend previous work on information diffusion in social networks. Table 2 provides a comparative analysis of our approach with key existing models.  
Our approach differs from previous models in several important ways. First, we systematically compare three distinct delay mechanisms (constant, uniform, exponential) across various network structures. This comparative approach is absent in most existing studies, which typically focus on a single delay mechanism. For example, [PERSON] et al. (2021, 2024) [8,11] incorporate warning timeliness in their population dynamics model but do not compare different delay distributions.  
Second, our identification of peak_speed_time as the dominant predictor (importance = 0.848) highlights the critical role of temporal dynamics in information diffusion. This finding extends beyond the typical structural focus found in models like those of [PERSON] and [PERSON] (2021) [16], which emphasize network distance and node influence.  
The performance of our Gradient Boosting model (R\({}^{2}\) = 0.847 \(\pm\) 0.032) compares favorably with existing approaches. [PERSON] et al. (2022) [12] report a 98.78% prediction accuracy for their ODID model on the Digg dataset, which appears superior. However, their evaluation uses a different metric and dataset, making direct comparison difficult. Our approach focuses on cross-validation robustness across diverse network types, providing more generalizable insights.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Key Features** & **Strengths** & **Limitations** & **Performance Metrics** \\ \hline \multirow{4}{*}{Our Model} & Multiple delay types, cross-validation on real and synthetic data, temporal dynamics focus & High predictive power (R\({}^{2}\) = 0.847), rigorous feature selection, strong temporal insights & Limited generalizability in verification & Coverage prediction (R\({}^{2}\)), feature stability \\ \hline \multirow{4}{*}{[PERSON] et al. (2021, 2024) [8,11]} & Population dynamics with warning timeliness, real-world crisis applications & Good fit to Weibo data, prediction capability & Limited delay mechanism variety, no cross-network testing & Curve fitting accuracy \\ \hline \multirow{4}{*}{SEI Model ([PERSON] et al., 2020) [9]} & Three-state transitions with exposed state & Captures pre-sharing exposure state, Twitter data & No delay variation analysis, limited feature importance & User state counts \\  & & & validation & \\ \hline Non-linear & Time-fractional & Models super-/sub-diffusion, high precision on Digg/Twitter & Complex implementation, no temporal feature analysis & Density prediction accuracy \\ \hline \multirow{4}{*}{ODID ([PERSON] et al., 2022) [12]} & Ordinary differential equations, friendship-based rather than geometric & A total 98.78% prediction accuracy, captures both temporal and spatial patterns & No delay mechanism comparison, limited network variety & Prediction accuracy vs. actual data \\ \hline \multirow{4}{*}{Fuzzy Sign-Aware ([PERSON] et al., 2023) [18]} & Multi-trust relationships, fuzzy logic implementation & Handles trust/distrust relations, natural multi-level trust & Limited to signed networks, no temporal dynamics focus & Prediction accuracy, coverage \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of information diffusion models.