In many real-world scenarios, an individual's local social network carries significant influence over the opinions they form and subsequently propagate. In this paper, we propose a novel diffusion model -- the Pressure Threshold model (PT) -- for dynamically simulating the spread of influence through a social network. This model extends the popular Linear Threshold (LT) model by adjusting a node's outgoing influence in proportion to the influence it receives from its activated neighbors. We examine the Influence Maximization (IM) problem under this framework, which involves selecting seed nodes that yield maximal graph coverage after a diffusion process, and describe how the problem manifests under the PT model. Experiments on real-world networks, supported by enhancements to the open-source network-diffusion library CyNetDiff, reveal that the PT model identifies seed sets distinct from those chosen by LT. Furthermore, the analyses show that densely connected networks amplify pressure effects far more strongly than sparse networks.

翻译：在许多现实场景中，个体的局部社交网络对其观点形成及后续传播具有显著影响。本文提出一种新颖的扩散模型——压力阈值模型（PT），用于动态模拟影响力在社交网络中的传播。该模型通过按比例调整节点从其激活邻居接收的影响力，进而调节该节点的对外影响力，从而扩展了经典的线性阈值（LT）模型。在此框架下，我们研究了影响力最大化（IM）问题——即选择在扩散过程后能实现最大图覆盖的种子节点，并阐述了该问题在PT模型中的具体表现形式。基于对开源网络扩散库CyNetDiff的改进，在真实网络上的实验表明：PT模型所识别的种子集合与LT模型选择的集合存在显著差异。进一步分析显示，紧密连接的网络比稀疏网络更能强烈地放大压力效应。