A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
This work addresses influence spread in social networks, offering an incremental improvement for applications like marketing or information dissemination.
The paper tackles the Influence Maximization problem on social networks by proposing the Pressure Threshold model, which extends the Linear Threshold Model to adjust outgoing influence based on received influence, and finds that it selects different seed nodes and amplifies effects more in dense networks.
In many real-world scenarios, an individual's local social network carries significant influence over the opinions they form and subsequently propagate to others. 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 new model extends the popular Linear Threshold Model (LT) by adjusting a node's outgoing influence proportional to the influence it receives from its activated neighbors. We address the Influence Maximization (IM) problem, which involves selecting the most effective seed nodes to achieve maximal graph coverage after a diffusion process, and how the problem manifests with the PT Model. Experiments conducted on real-world networks, facilitated by enhancements to the open-source network-diffusion Python library, CyNetDiff, demonstrate unique seed node selection for the PT Model when compared to the LT Model. Moreover, analyses demonstrate that densely connected networks amplify pressure effects more significantly than sparse networks.