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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Stutsman, Curt, Robson, Eliot W., Umrawal, Abhishek K.
Format:	Preprint
Published:	2025
Subjects:	Social and Information Networks Artificial Intelligence 05C85, 60J60, 68R05, 68R10, 68T01, 90C35 G.2.1; G.2.2; G.3; H.3.4; H.3.5; I.2.0; J.4
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866914438467026944
author	Stutsman, Curt Robson, Eliot W. Umrawal, Abhishek K.
author_facet	Stutsman, Curt Robson, Eliot W. Umrawal, Abhishek K.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_12822
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	A Pressure-Based Diffusion Model for Influence Maximization on Social Networks Stutsman, Curt Robson, Eliot W. Umrawal, Abhishek K. Social and Information Networks Artificial Intelligence 05C85, 60J60, 68R05, 68R10, 68T01, 90C35 G.2.1; G.2.2; G.3; H.3.4; H.3.5; I.2.0; J.4 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.
title	A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
topic	Social and Information Networks Artificial Intelligence 05C85, 60J60, 68R05, 68R10, 68T01, 90C35 G.2.1; G.2.2; G.3; H.3.4; H.3.5; I.2.0; J.4
url	https://arxiv.org/abs/2509.12822