# A Pressure-Based Diffusion Model for Influence Maximization on Social Networks

arXiv: https://arxiv.org/abs/2509.12822 · HTML: https://mlsanity.com/paper/2509.12822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots](https://mlsanity.com/paper/1703.06027/) (2017)
- [Fairness in Influence Maximization through Randomization](https://mlsanity.com/paper/2010.03438/) (2020)
- [CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models](https://mlsanity.com/paper/2404.17059/) (2024)
- [Fractional Budget Allocation for Influence Maximization under General Marketing Strategies](https://mlsanity.com/paper/2407.05669/) (2024)
- [Network Diffusion -- Framework to Simulate Spreading Processes in Complex Networks](https://mlsanity.com/paper/2405.18085/) (2024)
- [The Role of Network and Identity in the Diffusion of Hashtags](https://mlsanity.com/paper/2407.12771/) (2024)

## Built on by (most cited first, 1 of 1)

- [LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection](https://mlsanity.com/paper/2601.00933/) (2026)

## Abstract (arXiv)

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.