Working Paper Article Version 1 This version is not peer-reviewed

Multi-Winner Election Control via Social Influence: Hardness and Algorithms for Restricted Cases

Version 1 : Received: 27 August 2020 / Approved: 30 August 2020 / Online: 30 August 2020 (10:42:09 CEST)
Version 2 : Received: 6 September 2020 / Approved: 7 September 2020 / Online: 7 September 2020 (04:11:59 CEST)

How to cite: Abouei Mehrizi, M. Multi-Winner Election Control via Social Influence: Hardness and Algorithms for Restricted Cases. Preprints 2020, 2020080651 Abouei Mehrizi, M. Multi-Winner Election Control via Social Influence: Hardness and Algorithms for Restricted Cases. Preprints 2020, 2020080651

Abstract

Nowadays, many political campaigns are using social influence (SI) in order to convince voters to support/oppose a specific candidate/party. In election control via SI 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 SI 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.

Subject Areas

Computational Social Choice; Election Control; Multi-winner Election; Social Influence; Influence Maximization

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