A novel controllability method on temporal networks based on tree model

Abstract

Abstract Temporal networks have become instrumental in modeling dynamic systems across various disciplines, presenting unique challenges and opportunities in understanding and influencing their behavior. Controllability, a fundamental aspect of network dynamics, plays a pivotal role in manipulating these systems towards desired states. In this paper, we embark on a comprehensive exploration of controllability within the realm of temporal networks. A new method for controlling temporal networks is proposed, in which the intervention of all the dynamics of temporal networks can provide the possibility to speed up the network controllability processes. In the proposed method, the network dynamics are stored in the tree data structure to reduce the computational complexity of the algorithm for finding control nodes while maintaining essential information in controllable processes. Results show that the proposed algorithm with linear complexity of $${\varvec{O}}({{\varvec{N}}}^{2}{\varvec{l}}{\varvec{o}}{\varvec{g}}{\varvec{N}}{\Delta {\varvec{t}}}^{4})$$ O ( N 2 l o g N Δ t 4 ) . Evaluation against conventional methods on experimental datasets reveals notable improvements: a 41.8% reduction in the minimum number of control nodes, a 36.37% decrease in time of receiving fully control network, and a 38.5% reduction in control algorithm execution time compared to layered model-based control methods.

Keywords

• Controllability of temporal networks
• Temporal networks
• Driver Nodes Set
• Maximum Flow Algorithms
• Tree Model