What question did this study set out to answer?

The aim is to develop a game theory-based method to enhance controllability in dynamic systems within complex networks.

Robust deep nonnegative rank matrix factorization to enhancing the efficiency of controllability in temporal networks using mixed-strategy game theory

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial shows improved controllability in complex networks, suggesting more efficient control strategies.

The aim is to develop a game theory-based method to enhance controllability in dynamic systems within complex networks.
Proposed a game theory-based controllability method (GTCM) using a mixed-strategy game.
Designed an optimal algorithm leveraging game evolution theory for complex networks.
Simulated the GTCM method on real-world datasets and compared it with conventional approaches.
GTCM method achieved closer proximity to equilibrium with reduced convergence error compared to conventional methods.
Demonstrated full controllability in a shorter time with fewer driver nodes than other algorithms.
Established better payoff distribution through modeling nodes as mixed-strategy agents.

Zhang et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark