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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Vydáno v:	Physica A Ročník 528; s. 121430
Hlavní autoři:	Riquelme, Fabián, Gonzalez-Cantergiani, Pablo, Molinero, Xavier, Serna, Maria
Médium:	Journal Article Publikace
Jazyk:	angličtina
Vydáno:	Elsevier B.V 15.08.2019
Témata:	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
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Abstract	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.
AbstractList	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 Peer Reviewed 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.
ArticleNumber	121430
Author	Riquelme, Fabián Molinero, Xavier Gonzalez-Cantergiani, Pablo Serna, Maria
Author_xml	– sequence: 1 givenname: Fabián surname: Riquelme fullname: Riquelme, Fabián email: fabian.riquelme@uv.cl organization: Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Chile – sequence: 2 givenname: Pablo surname: Gonzalez-Cantergiani fullname: Gonzalez-Cantergiani, Pablo email: gonzalezcantergiani@gmail.com organization: Independent Researcher – sequence: 3 givenname: Xavier surname: Molinero fullname: Molinero, Xavier email: xavier.molinero@upc.edu organization: Mathematics Department and Barcelona Graduate School of Mathematics, Universitat Politècnica de Catalunya, Spain – sequence: 4 givenname: Maria surname: Serna fullname: Serna, Maria email: mjserna@cs.upc.edu organization: Computer Science Department and Barcelona Graduate School of Mathematics, Universitat Politècnica de Catalunya, Spain
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Contributor	Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals Universitat Politècnica de Catalunya. GRTJ - Grup de Recerca en Teoria de Jocs Universitat Politècnica de Catalunya. Departament de Matemàtiques Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
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Lett. doi: 10.1209/0295-5075/94/38001
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SubjectTerms	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
Title	The neighborhood role in the linear threshold rank on social networks
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