Complexity of equilibrium in competitive diffusion games on social networks

Etesami, S. Rasoul; Başar, Tamer

doi:10.1016/j.automatica.2016.01.063

Cited by 36 publications

(18 citation statements)

References 25 publications

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“…It is usually assumed that the seeding strategy of the competitors has already been fixed and is known. Nash equilibrium analysis has been proposed in the case in which all competitors may adapt their strategy in a game theoretic perspective [10].…”

Section: Related Workmentioning

confidence: 99%

Containment of rumor spread in complex social networks

Lan

Giua

2020

Information Sciences

135

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Rumors can propagate at great speed through social networks and produce significant damages. In order to control rumor propagation, spreading correct information to counterbalance the effect of the rumor seems more appropriate than simply blocking rumors by censorship or network disruption. In this paper, a competitive diffusion model, namely Linear Threshold model with One Direction state Transition (LT1DT), is proposed for modeling competitive information propagation of two different types in a same network. The problem of minimizing rumor spread in social networks is explored and a novel heuristic based on diffusion dynamics is proposed to solve this problem under the LT1DT. Experimental analysis on four different networks shows that the novel heuristic outperforms pagerank centrality. By seeding correct information in the proximity of rumor seeds, the novel heuristic performs as well as the greedy approach in scale-free and small-world networks but runs three orders of magnitude faster than the greedy approach.

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Section: Related Workmentioning

confidence: 99%

Containment of rumor spread in complex social networks

Lan

Giua

2020

Information Sciences

135

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“…Kermani et al [30] used evolutionary game theory to identify the most influential node in information propagation. Etesami et al [31] studied a class of games known as diffusion games that model the competitive behavior of a set of social actors on an undirected, connected social network.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks

Yang

Zhu

et al. 2019

Complexity

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To better control the scope of information propagation and understand its dynamic characteristics, we propose an information propagation model based on evolutionary game theory. The model can simulate an individual’s strategy selection in social networks when facing two pieces of competitive information, whereby “competitive information” is defined as two pieces of information which have the opposite meaning. First, a reasonable payoff function is designed for individuals based on pairwise interaction. Second, each individual selects a friend it trusts. Third, a probability value is used to indicate whether an individual imitates the strategy of the selected friend. In the model, we consider not only the heterogeneous influence of friends’ strategies on individual decision-making in the process of communication but also the attenuation of individuals’ attention to information when information about friends is received repeatedly. The simulation results show that our model can accurately simulate the propagation of two pieces of competitive information. Furthermore, we find that the basic payoff that accrues to individuals as a result of spreading their information and the network topology are two factors that significantly influence the propagation result. The results provide effective insights into how to better control and guide public opinion.

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“…A vast literature exists on multi-agent dynamics in social networks, although mostly focused on consensus and cooperation (see, e.g., [16], [24], [25], [26], [27] and bibliographies therein). Yet, in social dynamics, a very important role is played by competition [4], [35]: social agents often have conflicting goals [8], [18], [23], compete for shared resources [7] (a well-studied phenomenon in ecology [3], [32]), or rival for supremacy in social networks [17], [34], races [9], [10], economy [6], [20], [21], [29] and politics [31]. Antagonistic interactions [1], [2], [22] can arise when an agent obstructs or undermines another in the fight for survival or supremacy.…”

Section: Introductionmentioning

confidence: 99%

A switched model for mixed cooperative-competitive social dynamics

Blanchini

Casagrande

Giordano

et al. 2019

2019 18th European Control Conference (ECC)

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A multi-agent continuous-time nonlinear model of social behaviour allowing for both competition and cooperation is presented and analysed. The state of each agent is represented by its payoff, which the agent aims at maximising. The role of control variables is played by the model parameters, which account for the agents' decisions to either cooperate with or boycott the other agents and can vary in time within assigned intervals. Alliances and enmities can be established at any time, according to either a greedy or a longsighted criterion. The general nonlinear case is first considered. It is proved that, under realistic assumptions, the system evolution is bounded positive (no extinction) and there is a unique globallystable equilibrium point. As is somehow expected, the optimal decision for all agents corresponds to full cooperation (decision parameters kept at their positive maximum value) in the case of both shortsighted and farsighted criteria. This is not true if some parameters have negative upper bounds (meaning that some agents systematically boycott some others). Then, in the linear case, it is shown that the system is stable for arbitrarilyvarying decision parameters, provided that a Metzler matrix associated with full cooperation is Hurwitz. A characterisation of the long-term behaviour of the linear system is also provided. In particular, it is proved that, under stability conditions, a Nash equilibrium exists if a steady strategy is adopted. G.G. acknowledges support from the Aspasia grant and the DTF grant.

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Complexity of equilibrium in competitive diffusion games on social networks

Cited by 36 publications

References 25 publications

Containment of rumor spread in complex social networks

Containment of rumor spread in complex social networks

Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks

A switched model for mixed cooperative-competitive social dynamics

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