The problem of election control through social influence consists in finding a set of nodes in a social network of voters to be the starters of a political campaign aimed at supporting a particular target candidate. The voters reached by the campaign change their views on the candidates. The goal is to model the spread of the campaign in such a way as to maximize the chances of winning for the target candidate. Herein, differently from previous work, we consider that each voter is associated with a probability distribution over the candidates modeling the likelihood of the voter to vote for each candidate. In a first model we propose, we prove that, under the Gap-ETH, the problem cannot be approximated to within a factor better than , where n is the number of voters. In a second model, which is a slight relaxation of the first one, the problem instead admits a constant-factor approximation algorithm. Finally, we present simulations on both synthetic and real networks, comparing the results of our algorithm with those obtained by a standard greedy algorithm for Influence Maximization.

Election control through social influence with voters’ uncertainty

AboueiMehrizi, Mohammad;Coro, Federico;Cruciani, Emilio;D'Angelo, Gianlorenzo
2022-01-01

Abstract

The problem of election control through social influence consists in finding a set of nodes in a social network of voters to be the starters of a political campaign aimed at supporting a particular target candidate. The voters reached by the campaign change their views on the candidates. The goal is to model the spread of the campaign in such a way as to maximize the chances of winning for the target candidate. Herein, differently from previous work, we consider that each voter is associated with a probability distribution over the candidates modeling the likelihood of the voter to vote for each candidate. In a first model we propose, we prove that, under the Gap-ETH, the problem cannot be approximated to within a factor better than , where n is the number of voters. In a second model, which is a slight relaxation of the first one, the problem instead admits a constant-factor approximation algorithm. Finally, we present simulations on both synthetic and real networks, comparing the results of our algorithm with those obtained by a standard greedy algorithm for Influence Maximization.
2022
Computational social choice; Election control; Influence maximization; Social influence
File in questo prodotto:
File Dimensione Formato  
PostPrint_2022_JCombOptim_44_AboueiMehrizi.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Creative commons
Dimensione 282.28 kB
Formato Adobe PDF
282.28 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12571/24547
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact