From data to network structure&amp;mdash;&amp;mdash;Reconstruction of dynamic networks

Zhang, Zhaoyang; Yang, Chen; Mi, Yuanyuan; Hu, Gang

doi:10.1360/sspma2019-0127

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…And methods for dynamic network are also proposed [32]. More overviews about network reconstruction can be found in the literature [33][34][35][36]. Among them, CS is a method to solve the problems brought by high-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

The reconstruction on the game networks with binary-state and multi-state dynamics

Wang

Guo

2022

PLoS ONE

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Reconstruction of network is to infer the relationship among nodes using observation data, which is helpful to reveal properties and functions of complex systems. In view of the low reconstruction accuracy based on small data and the subjectivity of threshold to infer adjacency matrix, the paper proposes two models: the quadratic compressive sensing (QCS) and integer compressive sensing (ICS). Then a combined method (CCS) is given based on QCS and ICS, which can be used on binary-state and multi-state dynamics. It is found that CCS is usually a superior method comparing with compressive sensing, LASSO on several networks with different structures and scales. And it can infer larger node correctly than the other two methods. The paper is conducive to reveal the hidden relationship with small data so that to understand, predicate and control a vast intricate system.

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Section: Introductionmentioning

confidence: 99%

The reconstruction on the game networks with binary-state and multi-state dynamics

Wang

Guo

2022

PLoS ONE

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“…In recent years, researchers have explored approaches to inferring the interactions by solving ordinary differential equations (ODEs). Some detection methods can be used to reconstruct a linear dynamic network, [16][17][18][19] while expanding basis [20][21][22][23][24] and expanding variable [25,26] methods are proposed to detect a general nonlinear dynamic network. These methods not only reveal connections, but also provide information on local dynamics and coupling functions.…”

Section: Introductionmentioning

confidence: 99%

“…These methods not only reveal connections, but also provide information on local dynamics and coupling functions. For these methods, [20][21][22][23][24][25][26] solving ODEs requires the output data of the whole network. The existence of noises increases the difficulty of network reconstruction, sometimes, also leading to new approaches.…”

Section: Introductionmentioning

confidence: 99%

Inferring interactions of time-delayed dynamic networks by random state variable resetting

2022

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Time delays exist widely in real systems, and time-delayed interactions can result in abundant dynamic behaviors and functions in dynamic networks. Inferring the time delays and interactions is challenging due to systematic nonlinearity, noises, a lack of information, and so on. Recently, Shi et al. proposed a random state variable resetting method to detect the interactions in a continuous-time dynamic network. By arbitrarily resetting the state variable of a driving node, the equivalent coupling functions of the driving node to any response node in the network can be reconstructed. In this paper, we introduce this method in time-delayed dynamic networks. To infer actual time delays, the nearest neighbor correlation (NNC) function for a given time delay is defined. The significant increments of NNC originate from the delayed effect. Based on the increments, the time delays can be reconstructed and the reconstruction errors depend on the sampling time interval. After time delays are accurately identified, the equivalent coupling functions can also be reconstructed. The numerical results have fully verified the validity of the theoretical analysis.

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