Towards influence centrality: where to not add an edge in the network?

In this work, we consider a strongly connected group of individuals involved in decision-making. The opinions of the individuals evolve using the Friedkin-Johnsen (FJ) model. We consider that there are two competing ‘influencers’ (stubborn agents) vying for control over the final opinion of the group. We investigate the impact of modifying the network interactions on their respective control over the final opinions (influence centrality). We use signal flow graphs (SFG) to relate the network interactions with the influence that each ‘influencer’ exerts on others. We present the sufficient conditions on the edge modifications which lead to the increase of the influence of an ‘influencer’ at the expense of the other. Interestingly, the analysis also reveals the existence of redundant edge modifications that result in no change in the influence centrality of the network. We present several numerical examples to illustrate these results.

Authors

Institutions

Publication Details

Published
2025-06-24
DOI
https://doi.org/10.23919/ecc65951.2025.11186998
Primary Topic
Complex Systems and Decision Making
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Towards influence centrality: where to not add an edge in the network?

Aashi Shrinate, Twinkle Tripathy
Complex Systems and Decision Making
article

Towards influence centrality: where to not add an edge in the network?

Aashi Shrinate, Twinkle Tripathy
article en

Abstract

In this work, we consider a strongly connected group of individuals involved in decision-making. The opinions of the individuals evolve using the Friedkin-Johnsen (FJ) model. We consider that there are two competing ‘influencers’ (stubborn agents) vying for control over the final opinion of the group. We investigate the impact of modifying the network interactions on their respective control over the final opinions (influence centrality). We use signal flow graphs (SFG) to relate the network interactions with the influence that each ‘influencer’ exerts on others. We present the sufficient conditions on the edge modifications which lead to the increase of the influence of an ‘influencer’ at the expense of the other. Interestingly, the analysis also reveals the existence of redundant edge modifications that result in no change in the influence centrality of the network. We present several numerical examples to illustrate these results.

Indian Institute of Technology Kanpur (IN)
Openalex Percentile: Top 66%
Complex Systems and Decision Making
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.