Game-Theoretic Models of Information Diffusion in Social Networks

Game-theoretic models provide a framework for analysing how rational agents strategically choose actions to maximise their influence in a networked environment. In these models, players represent entities such as firms, political campaigns or social groups, each aiming to promote particular information or opinions. Core approaches extend classical diffusion paradigms—such as the Independent Cascade and Linear Threshold models—by embedding payoff structures, utility functions and strategic interaction. Equilibria, typically Nash equilibria, characterise the stable allocation of resources or seed nodes under competitive or cooperative settings. Heterogeneity in user preferences, network topology and information content plays a crucial role in determining optimal seeding strategies and the resulting diffusion dynamics. Recent advances examine dynamic adaptation of strategies, budget constraints, multi-round interactions and multi-player contests. Applications span viral marketing, misinformation mitigation and public-health messaging, revealing that network structure, timing and the nature of strategic incentives jointly shape global diffusion patterns.

Research from Nature Portfolio

Recent studies have integrated information-theoretic measures with control-theoretic insights to quantify and optimise information flows in complex networks. A novel framework links network topology to functional patterns of transfer, enabling the identification of critical links and nodes whose reconfiguration maximises information reach. This approach has been demonstrated in neural circuits, sensor arrays and social platforms, showing that targeted topological adjustments can enhance contagion or reroute influence under specified constraints. By unifying causal inference with network control, these methods pave the way for algorithmic design of interventions that steer diffusion towards desired outcomes.

Game-Theoretic Models of Information Diffusion in Social Networks publication trend

The graph below shows the total number of articles in game-theoretic models of information diffusion in social networks across all publications each year (not limited to Nature Index journals).

Technical terms

Nash equilibrium: A state in which no player can improve their payoff by unilaterally changing strategy.

Influence maximisation: The problem of selecting an initial set of nodes to maximise overall spread of information.

Independent Cascade model: A diffusion model in which activated nodes attempt to influence neighbours in independent trials.

Linear Threshold model: A diffusion model where nodes adopt information once accumulated influence exceeds a threshold.

Seed node: An initial adopter chosen to initiate the diffusion process based on strategic criteria.

References

1. A Game-Theoretic Approach for Modeling Competitive Diffusion over Social Networks. Games (2018).
2. Choosing Optimal Seed Nodes in Competitive Contagion. Frontiers in Big Data (2019).
3. DEVELOPING GAME THEORY-BASED METHODS FOR MODELING INFORMATION CONFRONTATION IN SOCIAL NETWORKS. Scientific Journal of Astana IT University (2024).
4. On quantification and maximization of information transfer in network dynamical systems. Scientific Reports (2023).