Controlling complex networks by path cover

With the rapid development of complex networks, their effective control has attracted widespread attention. To ensure controllability, it is necessary not only to identify driver nodes but also to determine an exact input matrix. In this paper, a method based on path cover is proposed to derive an exact input matrix that satisfies the Kalman rank condition for complex networks. By assigning sufficiently large edge strength to each path in the cover, the network becomes controllable through this input matrix. The method is applicable to complex networks that are directed or undirected, weighted or unweighted. For directed networks, controllability is ensured by controlling only the source nodes of each path. In contrast, for undirected networks, it suffices to control either the source or endpoint nodes within each identified path cover. Consequently, the resulting input matrix is simple and practical for implementation.

Authors

Institutions

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-03-20
DOI
https://doi.org/10.1209/0295-5075/ae4b63/pdf
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Controlling complex networks by path cover

Z. Z. Yuan, F. C. Fan, R. Pang
Springer Link (Chiba Institute of Technology)
Neural Networks Stability and Synchronization
article

Controlling complex networks by path cover

Z. Z. Yuan, F. C. Fan, R. Pang
article en

Abstract

With the rapid development of complex networks, their effective control has attracted widespread attention. To ensure controllability, it is necessary not only to identify driver nodes but also to determine an exact input matrix. In this paper, a method based on path cover is proposed to derive an exact input matrix that satisfies the Kalman rank condition for complex networks. By assigning sufficiently large edge strength to each path in the cover, the network becomes controllable through this input matrix. The method is applicable to complex networks that are directed or undirected, weighted or unweighted. For directed networks, controllability is ensured by controlling only the source nodes of each path. In contrast, for undirected networks, it suffices to control either the source or endpoint nodes within each identified path cover. Consequently, the resulting input matrix is simple and practical for implementation.

Springer Link (Chiba Institute of Technology)
Minnan Normal University (CN)
Openalex Percentile: Top 7%
Neural Networks Stability and Synchronization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.