A variational inequality framework for network games: Existence, uniqueness, convergence and sensitivity analysis
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Abstract (arXiv)
We provide a unified variational inequality framework for the study of fundamental properties of the Nash equilibrium in network games. We identify several conditions on the underlying network (in terms of spectral norm, infinity norm and minimum eigenvalue of its adjacency matrix) that guarantee existence, uniqueness, convergence and continuity of equilibrium in general network games with multidimensional and possibly constrained strategy sets. We delineate the relations between these conditions and characterize classes of networks that satisfy each of these conditions.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2017.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 2; 2026: 1.
9 papers.
- 2023Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamicscited by 8
- 2023Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updatescited by 4
- 2024Learning to Control Unknown Strongly Monotone Gamescited by 4
- 2024Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicitycited by 1
- 2025Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networkscited by 1
- 2022Optimization-Informed Neural Networkscited by 0
- 2023Stability of Multi-Agent Learning: Convergence in Network Games with Many Playerscited by 0
- 2025Diffusion Stochastic Learning Over Adaptive Competing Networkscited by 0
- 2026Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent Statescited by 0
- 2026Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States21 Jul 2026
- 2025Diffusion Stochastic Learning Over Adaptive Competing Networks28 Apr 2025
- 2025Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networks13 Mar 2025
- 2024Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity28 Oct 2024
- 2024Learning to Control Unknown Strongly Monotone Games30 Jun 2024
- 2023Stability of Multi-Agent Learning: Convergence in Network Games with Many Players26 Jul 2023
- 2023Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates8 Jun 2023
- 2023Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics23 Jan 2023
- 2022Optimization-Informed Neural Networks5 Oct 2022