Xiao Hai-Bing, Hu Feng, Li Peng-Yue, Song Yu-Rong, Zhang Zi-Ke
School of Computer, Qinghai Normal University, Xining 810008, China.
The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China.
Entropy (Basel). 2024 Nov 6;26(11):957. doi: 10.3390/e26110957.
Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state (S) into active (Sa) and inactive (Si) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks.
社交网络作为现代信息传播的核心平台，呈现出独特的用户聚类行为和状态转换机制，从而给传统信息传播模型带来了新的挑战。基于超图理论，本文通过引入一种专门为在线社交网络设计的新型超网络信息传播SSEIR模型，对传统的SEIR模型进行了扩充。该模型准确地表示了复杂的、多用户的、高阶的交互作用。它将传统的单一易感状态（S）转变为活跃状态（Sa）和非活跃状态（Si）。此外，它通过反应过程策略（RP策略）增强了传统信息传播机制，并制定了精细的微分动力学方程，有效地模拟了在线社交网络中的传播和扩散过程。本文运用平均场理论对SSEIR模型中的传播机制进行了全面的理论推导。通过仿真实验验证了该模型在各种网络结构中的有效性，并通过在真实网络数据集上的应用进一步验证了其实用性。结果表明，SSEIR模型在数据拟合和阐明超网络结构内信息传播的内部机制方面表现出色，进一步明确了在线社交超网络中信息传播的动态演化模式。本研究不仅丰富了信息传播的理论框架，也为在线社交网络中的新闻传播、舆论管理和谣言监测等实际应用提供了科学的理论基础。
