Playing with Thresholds on the Forward Linear Threshold Rank
Maria J. Blesa, Maria Serna

TL;DR
This paper investigates how different threshold settings in the Linear Threshold model affect influence dissemination and ranking in social networks, highlighting the impact of threshold schemes on influence and the effectiveness of PageRank and FLTR.
Contribution
It introduces an experimental analysis of influence thresholds in the Linear Threshold model, exploring various schemes and their effects on network influence rankings.
Findings
Threshold schemes significantly impact influence rankings.
PageRank and FLTR provide the best threshold assignments.
Ranking changes can be abrupt depending on the scheme.
Abstract
Social networks are the natural space for the spreading of information and influence and have become a media themselves. Several models capturing that diffusion process have been proposed, most of them based on the Independent Cascade (IC) model or on the Linear Threshold (LT) model. The IC model is probabilistic while the LT model relies on the knowledge of an actor to be convinced, reflected in an associated individual threshold. Although the LT-based models contemplate an individual threshold for each actor in the network, the existing studies so far have always considered a threshold of 0.5 equal in all actors (i.e., a simple majority activation criterion). Our main objective in this work is to start the study on how the dissemination of information on networks behaves when we consider other options for setting those thresholds and how many network actors end up being influenced…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Game Theory and Applications
