Evolutionary multitasking multilayer network reconstruction
Due to the multilayer nature of real-world systems, the problem of inferring multilayer
network structures from nonlinear and complex dynamical systems is prominent in many …
network structures from nonlinear and complex dynamical systems is prominent in many …
An evolutionary multiobjective framework for complex network reconstruction using community structure
The problem of inferring nonlinear and complex dynamical systems from available data is
prominent in many fields, including engineering, biological, social, physical, and computer …
prominent in many fields, including engineering, biological, social, physical, and computer …
Network reconstruction based on time series via memetic algorithm
K Wu, J Liu, D Chen - Knowledge-Based Systems, 2019 - Elsevier
Reconstructing the interacting structure of complex networks from available data is
fundamental to understanding and controlling its collective dynamics. From the perspective …
fundamental to understanding and controlling its collective dynamics. From the perspective …
Online reconstruction of complex networks from streaming data
The problem of reconstructing nonlinear and complex dynamical systems from available
data or time series is prominent in many fields, including engineering, physical, computer …
data or time series is prominent in many fields, including engineering, physical, computer …
[PDF][PDF] Community based network reconstruction for an evolutionary algorithm framework
V Suma - Journal of Artificial Intelligence, 2021 - scholar.archive.org
Inferring complex and non-linear dynamic system using the data that is available plays an
important role in many areas of work such as physical, social, biological and computer …
important role in many areas of work such as physical, social, biological and computer …
SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction
Complex network is a general model to represent the interactions within technological,
social, information, and biological interaction. Often, the direct detection of the interaction …
social, information, and biological interaction. Often, the direct detection of the interaction …
Sparse Bayesian learning for network structure reconstruction based on evolutionary game data
Network structure reconstruction is a fundamental problem for understanding, predicting and
controlling the behaviors of complex networked systems and has received growing attention …
controlling the behaviors of complex networked systems and has received growing attention …
[HTML][HTML] Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly available. Parallel to this
is the need for new methods of reconstructing (inferring) the structure of networks from time …
is the need for new methods of reconstructing (inferring) the structure of networks from time …
[HTML][HTML] A general deep learning framework for network reconstruction and dynamics learning
Many complex processes can be viewed as dynamical systems on networks. However, in
real cases, only the performances of the system are known, the network structure and the …
real cases, only the performances of the system are known, the network structure and the …
General optimization framework for accurate and efficient reconstruction of symmetric complex networks from dynamical data
The challenging problem of network reconstruction from dynamical data can in general be
formulated as an optimization task of solving multiple linear equations. Existing approaches …
formulated as an optimization task of solving multiple linear equations. Existing approaches …