Minority Becomes Majority in Social Networks

Auletta, Vincenzo; Caragiannis, Ioannis; Ferraioli, Diodato; Galdi, Clemente; Persiano, Giuseppe

doi:10.1007/978-3-662-48995-6_6

Cited by 35 publications

(34 citation statements)

References 18 publications

(24 reference statements)

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“…Given a network where the influence spread according to a variant of LTM in which each node has a fixed threshold, and all edges have the same weight, find an optimal way of bribing nodes or add/delete edges in order to make the majority of nodes to vote for a target candidate. A different line of research investigates a model in which each voter is associated with a preference list over the candidates, and it updates its list according to the majority of opinions of its neighbors in the network [3,5,7]. All the previous works on election control through IM consider single-winner voting systems.…”

Section: Related Workmentioning

confidence: 99%

Multi-winner Election Control via Social Influence

Mehrizi

D’Angelo

2020

Structural Information and Communication Complexity

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The extensive use of social media in political campaigns has motivated the recent study of election control problem in social networks.In an election, we are given a set of voters, each having a preference list over a set of candidates, that are distributed on a social network. The winners of the election are computed by aggregating the preference lists of voters according to a so-called voting rule. We consider a scenario where voters may change their preference lists as a consequence of the messages received by their neighbors in a social network. Specifically, we consider a political campaign that spreads messages in a social network in support or against a given candidate and the spreading follows a dynamic model for information diffusion. When a message reaches a voter, this latter changes its preference list according to an update rule. The election control problem asks to find a bounded set of nodes to be the starter of a political campaign in support (constructive problem) or against (destructive problem) a given target candidate c, in such a way that the margin of victory of c w.r.t. its most voted opponents is maximized. It has been shown that several variants of the problem can be solved within a constant factor approximation of the optimum, which shows that controlling elections by means of social networks is doable and constitutes a real problem for modern democracies. Most of the literature, however, focuses on the case of single-winner elections.In this paper, we define the election control problem in social networks for multi-winner elections with the aim of modeling parliamentarian elections. Differently from the single-winner case, we show that the multi-winner election control problem is NP -hard to approximate within any factor in both constructive and destructive cases. We then study a relaxation of the problem where votes are aggregated on the basis of parties (instead of single candidates), which is a variation of the so-called straight-party voting used in some real parliamentarian elections. We show that the latter problem remains NP -hard but can be approximated within a constant factor.

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Section: Related Workmentioning

confidence: 99%

Multi-winner Election Control via Social Influence

Mehrizi

D’Angelo

2020

Structural Information and Communication Complexity

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“…Understanding how opinions are formed and expressed in a social context has been object of extensive recent study, in AI and multiagent systems [Pryymak et al, 2012, Tsang and Larson, 2014, Grandi et al, 2015, Schwind et al, 2015, computer science at large [Acemoglu and Ozdaglar, 2011, Bindel et al, 2011, Mossel and Tamuz, 2017, Auletta et al, 2015, as well as, sociology, economics, physics, and epidemiology. In particular, the work of Friedkin and Johnsen [1990], which represents our starting point, has been largely studied recently and has then emerged as the principal model in the area.…”

Section: Related Workmentioning

confidence: 99%

Social pressure in opinion dynamics

Ferraioli

Ventre

2019

Theoretical Computer Science

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Motivated by privacy and security concerns in online social networks, we study the role of social pressure in opinion dynamics. These are dynamics, introduced in economics and sociology literature, that model the formation of opinions in a social network. We enrich one of the most classical opinion dynamics, by introducing the pressure, increasing with time, to reach an agreement. We prove that for clique social networks, the dynamics always converges to consensus (no matter the level of noise) if the social pressure is high enough. Moreover, we provide (tight) bounds on the speed of convergence; these bounds are polynomial in the number of nodes in the network provided that the pressure grows sufficiently fast. We finally look beyond cliques: we characterize the graphs for which consensus is guaranteed, and make some considerations on the computational complexity of checking whether a graph satisfies such a condition.

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“…Several relevant works about opinion formation games appeared recently [14,26,9,2]. In particular, Chierichetti et al [14] also consider finite opinion games, but they try to extend the model to more than two available opinions.…”

Section: Related Workmentioning

confidence: 99%

Decentralized dynamics for finite opinion games

Ferraioli

Goldberg

Ventre

2016

Theoretical Computer Science

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Game theory studies situations in which strategic players can modify the state of a given system, in the absence of a central authority. Solution concepts, such as Nash equilibrium, have been defined in order to predict the outcome of such situations. In multi-player settings, it has been pointed out that to be realistic, a solution concept should be obtainable via processes that are decentralized and reasonably simple. Accordingly we look at the computation of solution concepts by means of decentralized dynamics. These are algorithms in which players move in turns to decrease their own cost and the hope is that the system reaches an “equilibrium” quickly.\ud \ud We study these dynamics for the class of opinion games, recently introduced by Bindel et al. [FOCS, 2011]. These are games, important in economics and sociology, that model the formation of an opinion in a social network. We study best-response dynamics and show upper and lower bounds on the convergence to Nash equilibria. We also study a noisy version of best-response dynamics, called logit dynamics, and prove a host of results about its convergence rate as the noise in the system varies. To get these results, we use a variety of techniques developed to bound the mixing time of Markov chains, including coupling, spectral characterizations and bottleneck ratio

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Minority Becomes Majority in Social Networks

Cited by 35 publications

References 18 publications

Multi-winner Election Control via Social Influence

Multi-winner Election Control via Social Influence

Social pressure in opinion dynamics

Decentralized dynamics for finite opinion games

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