Controllability of Kronecker product networks

Hao, Yuqing; Wang, Qingyun; Duan, Zhisheng; Chen, Guanrong

doi:10.1016/j.automatica.2019.108597

Cited by 32 publications

(10 citation statements)

References 30 publications

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“…Graph node partitions were subsequently employed to characterize the upper bounds on the dimension of the controllable subspace of multi-agent networks [5], [6], [7]; analogous lower bounds have also been derived using distance partitions [5], [8]. Due to the difficulty in analyzing controllability of general networks, controllability for special classes of networks has been an active area of research [9], [10], [11], [12], [13], [14]. Recently, controllability of multi-agent system on signed networks (where cooperative and competitive interactions coexist) has also received attention.…”

Section: Introductionmentioning

confidence: 99%

On the Controllability of Matrix-Weighted Networks

Pan

Shao

Mesbahi

et al. 2020

IEEE Control Syst. Lett.

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This letter examines the controllability of consensus dynamics on matrix-weighed networks from a graph-theoretic perspective. Unlike the scalar-weighted networks, the rank of weight matrix introduces additional intricacies into characterizing the dimension of controllable subspace for such networks. Specifically, we investigate how the definiteness of weight matrices influences the dimension of the controllable subspace. In this direction, graph-theoretic characterizations of the lower and upper bounds on the dimension of the controllable subspace are provided by employing, respectively, distance partition and almost equitable partition of matrix-weighted networks. Furthermore, the structure of an uncontrollable input for such networks is examined. Examples are then provided to demonstrate the theoretical results.

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Section: Introductionmentioning

confidence: 99%

On the Controllability of Matrix-Weighted Networks

Pan

Shao

Mesbahi

et al. 2020

IEEE Control Syst. Lett.

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“…Extensions of KPGM also include, for instance, a tied KPGM which accounts for variance in network populations (Moreno et al, 2010), a block-KPGM capturing a fractal structure observed in many real world networks (Moreno et al, 2013), an enhanced version of the KPGM which uses random noise for smoothing (Seshadhri et al, 2013), and a mixture-model based KPGM (Mahapatra & Chandola, 2015). Recently, Hao et al (2019) study the controllability of KPGM, while Ma et al (2019) investigate the existence of restricted edge connectivity, that is, the minimum number of edges whose deletion disconnects the graph, of KPGM.…”

Section: Kronecker Product Graph Modelmentioning

confidence: 99%

Community detection in complex networks: From statistical foundations to data science applications

Dey

Tian

Gel

2021

WIREs Computational Stats

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Identifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis. This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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“…The controllability of heterogeneous networked systems was investigated in [27]. The controllability conditions for networked systems with more complicated structure, such as deep-coupling networks [28] and Kronecker product networks [29] were also derived.…”

Section: Introductionmentioning

confidence: 99%

Controllability of Networked Sampled-data Systems

Yang¹,

Wang²,

Wang³

2022

Preprint

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The controllability of networked sampled-data systems with zero-order samplers on the control and transmission channels is explored, where single-and multi-rate sampling patterns are considered, respectively. The effects of sampling on controllability of networked systems are analyzed, with some sufficient and/or necessary controllability conditions derived. Different from the sampling control of single systems, the pathological sampling of node systems could be eliminated by the network structure and inner couplings under some conditions. While for singular topology matrix, the pathological sampling of single nodes will cause the entire system to lose controllability. Moreover, controllability of networked systems with specific node dynamics will not be affected by any periodic sampling. All the results indicate that whether a networked system is under pathological sampling or not is jointly determined by mutually coupled factors.

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Controllability of Kronecker product networks

Cited by 32 publications

References 30 publications

On the Controllability of Matrix-Weighted Networks

On the Controllability of Matrix-Weighted Networks

Community detection in complex networks: From statistical foundations to data science applications

Controllability of Networked Sampled-data Systems

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