Identifying the Most Influential Driver Nodes for Pinning Control of Multi-Agent Systems with Time-Varying Topology
Identifying the most influential driver nodes to guarantee the fastest synchronization speed is a key topic in pinning control of multi-agent systems. This paper develops a methodology to find the most influential pinning nodes under time-varying topologies. First, we provide the pinning control synchronization conditions of multi-agent systems. Second, a method is proposed to identify the best driver nodes that can guarantee the fastest synchronization speed under periodically switched systems. We show that the determination of the best driver nodes is independent of the system matrix under certain conditions. Finally, we develop a method to estimate the switching frequency threshold that can make the selected best driver nodes remain the same as the average system. Numerical simulations reveal the feasibility of these methods.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A graph neural network based on feature network for identifying influential nodes
Identifying influential nodes in complex networks is of great importance, and has many applications in practice. For example, finding influential nodes in e-commerce network can provide merchants with customers with stro…
Graph Neural NetworkProportional Selection in Networks
We address the problem of selecting $k$ representative nodes from a network, aiming to achieve two objectives: identifying the most influential nodes and ensuring the selection proportionally reflects the network's diver…
DiversityIdentifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis
Identifying important actors (or nodes) in a two-mode network often remains a crucial challenge in mining, analyzing, and interpreting real-world networks. While traditional bipartite centrality indices are often used to…
Influence Maximization in Hypergraphs Using A Genetic Algorithm with New Initialization and Evaluation Methods
Influence maximization (IM) is a crucial optimization task related to analyzing complex networks in the real world, such as social networks, disease propagation networks, and marketing networks. Publications to date abou…
MarketingFinding influential nodes for integration in brain networks using optimal percolation theory
Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental p…