Social Network Dynamics

The study evaluates network growth models, with the 'Forest Fire Model' demonstrating superior characteristics for trust anti-decay. This model exhibits power-law growth of triadic closures, high clustering coefficients, and short average path lengths, all of which contribute to a robust and stable trust environment. Understanding this evolutionary process is key to proactive platform management.

Different network growth models exhibit distinct behaviors regarding trust decay. The Forest Fire Model, while ideal in many aspects, shows critical risk points that need monitoring. ER Random Graphs offer robustness but lack dynamic growth, while BA Scale-Free Networks introduce a centralization effect that can stabilize trust but at the cost of local clustering equilibrium. Selecting the appropriate model or hybrid strategy is crucial for tailored trust management.

Applying the dynamic triplet model to Sina Weibo's attention network, we successfully extracted the largest connected component and performed embedding learning. The model effectively captures network structural characteristics, including social homogeneity and triplet closure mechanisms. This practical validation demonstrates its capability to monitor and predict trust evolution in real social media environments, enabling proactive interventions to prevent systemic trust decay. The loss function's smooth decrease to a low, stable level confirms the model's effective training.