The neighborhood role in the linear threshold rank on social networks

Centrality and influence spread are two of the most studied concepts in social network analysis. Several centrality measures, most of them, based on topological criteria, have been proposed and studied. In recent years new centrality measures have been defined inspired by the two main influence spre...

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Bibliographic Details
Published in:Physica A Vol. 528; p. 121430
Main Authors: Riquelme, Fabián, Gonzalez-Cantergiani, Pablo, Molinero, Xavier, Serna, Maria
Format: Journal Article Publication
Language:English
Published: Elsevier B.V 15.08.2019
Subjects:
05 Combinatorics
05C Graph theory
68 Computer science
68R Discrete mathematics in relation to computer science
91 Game theory, economics, social and behavioral sciences
91D Mathematical sociology
Centrality
Classificació AMS
Influència social
Investigació operativa
Linear threshold model
Matemàtiques i estadística
Mathematical models
Models matemàtics
Neighborhood
Social influence
Social network
Social networks
Spread of influence
Teoria de jocs
Xarxes socials
Àrees temàtiques de la UPC
91D30
05C22
Linear threshold model
Centrality
Social network
68R10
Spread of influence
Neighborhood
ISSN:0378-4371, 1873-2119
Online Access:Get full text
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Summary:Centrality and influence spread are two of the most studied concepts in social network analysis. Several centrality measures, most of them, based on topological criteria, have been proposed and studied. In recent years new centrality measures have been defined inspired by the two main influence spread models, namely, the Independent Cascade Model (IC-model) and the Linear Threshold Model (LT-model). The Linear Threshold Rank (LTR) is defined as the total number of influenced nodes when the initial activation set is formed by a node and its immediate neighbors. It has been shown that LTR allows to rank influential actors in a more distinguishable way than other measures like the PageRank, the Katz centrality, or the Independent Cascade Rank. In this paper we propose a generalized LTR measure that explore the sensitivity of the original LTR, with respect to the distance of the neighbors included in the initial activation set. We appraise the viability of the approach through different case studies. Our results show that by using neighbors at larger distance, we obtain rankings that distinguish better the influential actors. However, the best differentiating ranks correspond to medium distances. Our experiments also show that the rankings obtained for the different levels of neighborhood are not highly correlated, which validates the measure generalization. •Linear Threshold Rank is a centrality measure to identify influential actors on a social network.•We generalize the LTR measure to expand the neighbors distance in the initial activation set.•We appraise the viability of the approach through different case studies.•Using neighbors at larger distance, the rankings distinguish better the influential actors.•The generalization is validated since the rankings for different levels are not highly correlated.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2019.121430