On the Non-progressive Spread of Influence through Social Networks

Fazli, MohammadAmin; Ghodsi, Mohammad; Habibi, Jafar; Khalilabadi, Pooya Jalaly; Mirrokni, Vahab; Sadeghabad, Sina Sadeghian

doi:10.1007/978-3-642-29344-3_27

Cited by 18 publications

(16 citation statements)

References 24 publications

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“…Remark 1: The diffusion process as defined above is a particular case of progressive diffusion processes, where the state of the nodes does not change after adoption. This is in contrast to other types of processes known as non progressive processes [11].…”

Section: A Game Modelmentioning

confidence: 76%

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Complexity of equilibrium in diffusion games on social networks

Etesami

Basar

2014

2014 American Control Conference

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Abstract-In this paper, we consider the competitive diffusion game, and study the existence of its pure-strategy Nash equilibrium when defined over general undirected networks. We first determine the set of pure-strategy Nash equilibria for two special but well-known classes of networks, namely the lattice and the hypercube. Characterizing the utility of the players in terms of graphical distances of their initial seed placements to other nodes in the network, we show that in general networks the decision process on the existence of purestrategy Nash equilibrium is an NP-hard problem. Following this, we provide some necessary conditions for a given profile to be a Nash equilibrium. Furthermore, we study players' utilities in the competitive diffusion game over Erdos-Renyi random graphs and show that as the size of the network grows, the utilities of the players are highly concentrated around their expectation, and are bounded below by some threshold based on the parameters of the network. Finally, we obtain a lower bound for the maximum social welfare of the game with two players, and study sub-modularity of the players' utilities.

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Section: A Game Modelmentioning

confidence: 76%

“…virus spreading [8], [9]. One of the challenges in such models has been obtaining the solution to the best seed placement problem, which has been extensively studied for different processes [10], [11], [12] and [13].…”

Section: Introductionmentioning

confidence: 99%

Complexity of equilibrium in diffusion games on social networks

Etesami

Basar

2014

2014 American Control Conference

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“…The study of the spread of influence in complex networks has experienced a surge of interest in the last few years [4,9,31,34,35,36,39,44]. The algorithmic question of choosing the target set of size k that activates the most number of nodes in the context of viral marketing was first posed by Domingos and Richardson [26].…”

Section: Related Work and Our Resultsmentioning

confidence: 99%

Time-Bounded Influence Diffusion with Incentives

Cordasco

Gargano

Peters

et al. 2018

Structural Information and Communication Complexity

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A widely studied model of influence diffusion in social networks represents the network as a graph G = (V, E) with an influence threshold t(v) for each node. Initially the members of an initial set S ⊆ V are influenced. During each subsequent round, the set of influenced nodes is augmented by including every node v that has at least t(v) previously influenced neighbours. The general problem is to find a small initial set that influences the whole network. In this paper we extend this model by using incentives to reduce the thresholds of some nodes. The goal is to minimize the total of the incentives required to ensure that the process completes within a given number of rounds. The problem is hard to approximate in general networks. We present polynomial-time algorithms for paths, trees, and complete networks.

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“…virus spreading [23], [22]. One of the challenges in such models has been obtaining the solution to the best seed placement problem, which has been extensively studied for different processes [35], [14], [4] and [17].…”

Section: Introductionmentioning

confidence: 99%

Complexity of equilibrium in competitive diffusion games on social networks

Etesami

Başar

2016

Automatica

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In this paper, we consider the competitive diffusion game, and study the existence of its pure-strategy Nash equilibrium when defined over general undirected networks. We first determine the set of pure-strategy Nash equilibria for two special but wellknown classes of networks, namely the lattice and the hypercube. Characterizing the utility of the players in terms of graphical distances of their initial seed placements to other nodes in the network, we show that in general networks the decision process on the existence of pure-strategy Nash equilibrium is an NP-hard problem. Following this, we provide some necessary conditions for a given profile to be a Nash equilibrium. Furthermore, we study players' utilities in the competitive diffusion game over Erdos-Renyi random graphs and show that as the size of the network grows, the utilities of the players are highly concentrated around their expectation, and are bounded below by some threshold based on the parameters of the network. Finally, we obtain a lower bound for the maximum social welfare of the game with two players, and study sub-modularity of the players' utilities.

show abstract

On the Non-progressive Spread of Influence through Social Networks

Cited by 18 publications

References 24 publications

Complexity of equilibrium in diffusion games on social networks

Complexity of equilibrium in diffusion games on social networks

Time-Bounded Influence Diffusion with Incentives

Complexity of equilibrium in competitive diffusion games on social networks

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