scholarly journals Low-dimensional functionality of complex network dynamics: Neurosensory integration in theCaenorhabditiselegansconnectome

2014 ◽  
Vol 89 (5) ◽  
Author(s):  
James Kunert ◽  
Eli Shlizerman ◽  
J. Nathan Kutz
Keyword(s):  
Complex Network ◽  
Network Dynamics ◽  
Low Dimensional

Community detection in complex network by network embedding and density clustering

2021 ◽  
pp. 1-12
Author(s):  
JinFang Sheng ◽  
Huaiyu Zuo ◽  
Bin Wang ◽  
Qiong Li
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Dimensional Space ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Network Embedding ◽  
Detection Algorithms ◽  
Density Clustering ◽  
Community Detection Algorithm ◽  
Low Dimensional

 In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

On the Proper Description of Complex Network Dynamics

2018 ◽  
Author(s):  
Chun-Lin Yang ◽  
C. Steve Suh
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Collective Behavior ◽  
Local Level ◽  
Network Dynamics ◽  
Global Dynamics ◽  
Statistical Entropy ◽  
Network Systems ◽  
Local Dynamics ◽  
The Difference

Real-world networks are dynamical complex network systems. The dynamics of a network system is a coupling of the local dynamics with the global dynamics. The local dynamics is the time-varying behaviors of ensembles at the local level. The global dynamics is the collective behavior of the ensembles following specific laws at the global level. These laws include basic physical principles and constraints. Complex networks have inherent resilience that offsets disturbance and maintains the state of the system. However, when disturbance is potent enough, network dynamics can be perturbed to a level that ensembles no longer follow the constraint conditions. As a result, the collective behavior of a complex network diminishes and the network collapses. The characteristic of a complex network is the response of the system which is time-dependent. Therefore, complex networks need to account for time-dependency and obey physical laws and constraints. Statistical mechanics is viable for the study of multi-body dynamic systems having uncertain states such as complex network systems. Statistical entropy can be used to define the distribution of the states of ensembles. The difference between the states of ensembles define the interaction between them. This interaction is known as the collective behavior. In other words statistical entropy defines the dynamics of a complex network. Variation of entropy corresponds to the variation of network dynamics and vice versa. Therefore, entropy can serve as an indicator of network dynamics. A stable network is characterized by a specific entropy while a network on the verge of collapse is characterized by another. As the collective behavior of a complex network can be described by entropy, the correlation between the statistical entropy and network dynamics is investigated.

Parameter identification of complex network dynamics

2021 ◽  
Author(s):  
Arian Bakhtiarnia ◽  
Ali Fahim ◽  
Ehsan Maani Miandoab
Keyword(s):  
Parameter Identification ◽  
Complex Network ◽  
Network Dynamics

A Review on Complex Network Dynamics in Evolutionary Algorithm

2016 ◽  
Author(s):  
Long Sheng ◽  
Xiaoyun Guang ◽  
Feng Chen ◽  
Hui Wang ◽  
Kang Gao
Keyword(s):  
Evolutionary Algorithm ◽  
Complex Network ◽  
Network Dynamics

scholarly journals Network dynamics underlying OFF responses in the auditory cortex

2019 ◽  
Author(s):  
Giulio Bondanelli ◽  
Thomas Deneux ◽  
Brice Bathellier ◽  
Srdjan Ostojic
Keyword(s):  
Auditory Cortex ◽  
Single Cell ◽  
Network Model ◽  
Network Dynamics ◽  
Population Responses ◽  
Two Photon ◽  
Cortical Areas ◽  
Population Analyses ◽  
Spatio Temporal ◽  
Low Dimensional

AbstractAcross sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of network dynamics and propose a network model that generates transient OFF responses through recurrent interactions. To test this model, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli. We found that the network model accounts for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activate mostly orthogonal dimensions in the neural state-space. In contrast, a single-cell mechanism explains some prominent features of the data, but does not account for the structure across stimuli and trials captured by the network model.

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