# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 6 Discussion
### Theoretical Implications  
Our findings have several important theoretical implications for understanding information diffusion in social networks with delay mechanisms.  
* First, the dominant role of peak_speed_time in our models suggests a \"critical momentum\" theory of information diffusion. This theory posits that the timing of the maximum diffusion speed, rather than static network properties, determines the ultimate information coverage. One question emerging from our research is whether competing information concepts could help explain differential success rates in diffusion processes across varied network structures.
* Second, the consistent significance of spread probability (p_spread) across all datasets supports transmission-centric rather than structure-centric diffusion theories. While network structure matters, the likelihood of information sharing between connected individuals appears to be more fundamental. This contrasts with structure-focused approaches like those of [PERSON] et al. (2019) [17], who emphasized the topological dimensions of networks.
* Third, our finding that the delay mechanism type (constant, uniform, exponential) has no statistically significant effect contradicts the assumptions in some existing models. For example, [PERSON] et al. (2024) [14] developed elaborate state transitions in their SEIHR model, yet our results suggest that the specific delay distribution may be less important than previously thought. What matters more is the integration of some delay mechanisms rather than which specific type is used.
* Fourth, our verification analysis reveals challenges in transferring models between contexts, despite stability in feature importance rankings. This supports the contextual diffusion theory proposed by [PERSON] et al. (2023) [13], who argued that diffusion patterns depend on specific community characteristics rather than universal laws.  
The finding that delay mechanism type (constant, uniform, exponential) does not significantly affect information coverage warrants further examination. Several potential explanations may account for this counterintuitive result:  
First, the dominance of network structural properties and spread probability may overshadow delay type effects. In complex networks, the fundamental connectivity patterns and transmission likelihood could determine most of the variance in diffusion outcomes, regardless of how delays are distributed.  
Second, users' information consumption behaviors may naturally normalize temporal variations. Social media users typically check platforms at irregular intervals dictated by personal habits and external schedules rather than through continuous monitoring. This inherent behavioral variability may diminish the effects of specific programmed delay distributions.  
Third, there may be a threshold effect where the presence of any delay mechanism is more important than its specific distribution. Once some delay is introduced into the diffusion process, the specific pattern of delays may become secondary to other factors.  
Finally, methodological considerations such as parameter settings for delay distributions could influence this finding. While we use comparable mean delay values across mechanisms, the effective ranges of the delays experienced in different models might need more precise calibration in future work.