Abstract
Nowadays, many political campaigns are using social influence in order to convince voters to support/oppose a specific candidate/party. In election control via social influence problem, an attacker tries to find a set of limited influencers to start disseminating a political message in a social network of voters. A voter will change his opinion when he receives and accepts the message. In constructive case, the goal is to maximize the number of votes/winners of a target candidate/party, while in destructive case, the attacker tries to minimize them. Recent works considered the problem in different models and presented some hardness and approximation results. In this work, we consider multi-winner election control through social influence on different graph structures and diffusion models, and our goal is to maximize/minimize the number of winners in our target party. We show that the problem is hard to approximate when voters’ connections form a graph, and the diffusion model is the linear threshold model. We also prove the same result considering an arborescence under independent cascade model. Moreover, we present a dynamic programming algorithm for the cases that the voting system is a variation of straight-party voting, and voters form a tree.
Highlights
Social media is an integral part of nowadays life
We presented a polynomial-time algorithm based on the dynamic programming approach to find the maximum difference of votes for our target party before and after diffusion
We know that S ⊆ V after the diffusion; otherwise, if there is a node v0 ∈ V 0 ∩ S we can replace it with its incoming neighbor v ∈ V such that (v, v0 ) ∈ E0 and we get at least the same value for MoVc, DoWc
Summary
Social media is an integral part of nowadays life. No one can ignore the effect of social media on different aspects of our life. Considered the problem when the attacker knows a probability distribution over the candidates instead of the exact preferences list, under LTM [8] They showed that maximizing/minimizing the expected probability to vote for a target candidate is hard to approximate within any constant factor under unique game with small set expansion conjecture. Abouei Mehrizi and D’Angelo showed that in multi-winner elections, when the manipulator wants to maximize/minimize the number of winners in his target party, the problem is inapproximable under ICM, except P = NP [9] They presented some constant factor approximation algorithms when the voting system is similar to the straight-party voting.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
