A Mathematical Analysis of Network Controllability Through Driver Nodes

Chin, Sang; Cohen, Jonathan D.; Albin, Alison; Hayvanovych, Mykola; Reilly, Elizabeth P.; Brown, Gavin; Harer, Jacob

doi:10.1109/tcss.2017.2698725

Cited by 6 publications

(9 citation statements)

References 11 publications

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“…Serial number method: Directly count the number of the grid marks, from the upper left corner to 1, in order from top to bottom, from left to right, the position p can also use (5) to convert into the Cartesian coordinate(x, y):…”

Section: Application Of A* Algorithmmentioning

confidence: 99%

“…In (5), mod represents the remainder of p/10, and int represents the integer of p/10 [17]. This paper uses the serial number method and the process of algorithm's application is as follows:Take a 10 * 10 grid diagram as an example.…”

Section: Application Of A* Algorithmmentioning

confidence: 99%

“…; node-based methods, such as A-star(A*), D*, etc. ; mathematical models-based methods [5], [6], such as mixed integer nonlinear programming (MILP); biological heuristic algorithm-based, such as genetic algorithm(GA), particle swarm algorithm [7]. Liu et al [8] proposed a super-peer-based coordinated service provision to improve the scalability and robustness of intelligent systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Path Planning System for Smart Cars Used in Education

Guang-yu

Niu

Tian

et al. 2020

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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In this paper, we developed a path planning system for smart cars for teaching electronic engineering or computer science, which consists of the interactive platform for smart cars development and path planning. Designed by Visual C++, the interactive platform can call Matlab engine, allows users to choose path optimization algorithms such as genetic or A-star(A*) algorithm for different tasks and control smart cars through serial ports. The simulation and practice demonstrate that our interactive platform can help learners to plan paths and control intelligent vehicles without specially designing a user interface.

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Section: Application Of A* Algorithmmentioning

confidence: 99%

Section: Application Of A* Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Path Planning System for Smart Cars Used in Education

Guang-yu

Niu

Tian

et al. 2020

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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“…With deeper understanding of the controllability of complex networks [18][19][20][21][22][23][24][25][26], more scholars have begun to explore the ability to control complex networks through various indicators. For example, Liu et al [18] addressed the structural controllability of arbitrary complex directed networks, identifying a minimal set of driver nodes that can guide the system to any desired state.…”

Section: Introductionmentioning

confidence: 99%

“…Jia et al [20] proposed the concept of node control capability in the further study; that is, the possibility of node becoming a driving node was calculated. Many research works have been published under the theoretical framework of structural controllability [21][22][23][24][25][26]. However, these quantitative controllability methods only roughly measure the dimensionality of controllable subsystems; they could not better distinguish the controllability of systems with the same number of dimensions of controllable subsystems.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Controllability Index of Complex Networks

Wang

Zhang

Han

et al. 2018

Advances in Mathematical Physics

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In this paper, the controllability issue of complex network is discussed. A new quantitative index using knowledge of control centrality and condition number is constructed to measure the controllability of given networks. For complex networks with different controllable subspace dimensions, their controllability is mainly determined by the control centrality factor. For the complex networks that have the equal controllable subspace dimension, their different controllability is mostly determined by the condition number of subnetworks’ controllability matrix. Then the effect of this index is analyzed based on simulations on various types of network topologies, such as ER random network, WS small-world network, and BA scale-free network. The results show that the presented index could reflect the holistic controllability of complex networks. Such an endeavour could help us better understand the relationship between controllability and network topology.

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Effects of time horizons on influence maximization in the voter dynamics

Brede

Restocchi

Stein

2018

Journal of Complex Networks

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In this paper we analyze influence maximization in the voter model with an active strategic and a passive influencing party in non-stationary settings. We thus explore the dependence of optimal influence allocation on the time horizons of the strategic influencer. We find that on undirected heterogeneous networks, for short time horizons, influence is maximized when targeting low-degree nodes, while for long time horizons influence maximization is achieved when controlling hub nodes. Furthermore, we show that for short and intermediate time scales influence maximization can exploit knowledge of (transient) opinion configurations. More in detail, we find two rules. First, nodes with states differing from the strategic influencer's goal should be targeted. Second, if only few nodes are initially aligned with the strategic influencer, nodes subject to opposing influence should be avoided, but when many nodes are aligned, an optimal influencer should shadow opposing influence.

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A Mathematical Analysis of Network Controllability Through Driver Nodes

Cited by 6 publications

References 11 publications

Path Planning System for Smart Cars Used in Education

Path Planning System for Smart Cars Used in Education

Quantitative Controllability Index of Complex Networks

Effects of time horizons on influence maximization in the voter dynamics

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