提出压力阈值模型，更真实模拟社交网络影响力传播。
A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
- 基于邻居影响动态调整节点传播力，改进传统线性阈值模型。
- 实验显示压力效应在密集网络中比稀疏网络强得多。
- 适合研究社交影响力扩散与种子节点选择的学者参考。
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。本文提出一种新的扩散模型——压力阈值模型（PT），用于动态模拟社交网络中的影响力传播。该模型在流行的线性阈值（LT）模型基础上，将节点的传出影响力与其接收的激活邻居影响成比例调整。我们研究了在此框架下的影响力最大化（IM）问题，即选择能最大化扩散后图覆盖范围的种子节点，并描述了该问题在PT模型下的表现形式。在真实社交网络上的实验，结合对开源网络扩散库CyNetDiff的增强，表明在PT模型下采用贪心算法进行IM所选的种子集与LT模型显著不同。此外分析显示，密集连接的网络中压力效应的放大作用远强于稀疏网络。
原文摘要 · Abstract (English)
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 greedy IM under PT can yield seed sets distinct from those under LT. Furthermore, the analyses show that densely connected networks amplify pressure effects far more strongly than sparse networks.
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