Simulation of information propagation for vehicles in physical communication network models

2004 ◽  
Author(s):  
H. Sasaki ◽  
H. Iwasaki ◽  
T. Suda
Keyword(s):  
Communication Network ◽  
Network Models ◽  
Information Propagation

Modern Diffusion of Products with Complex Network Models

2011 ◽  
pp. 119-133
Author(s):  
Atsushi Tanaka
Keyword(s):  
Complex Networks ◽  
Marketing Strategy ◽  
Complex Network ◽  
Network Models ◽  
Information Propagation ◽  
Multiagent Simulation ◽  
Product Marketing ◽  
Winner Takes All ◽  
Complex Network Models

In this chapter, some important matters of complex networks and their models are reviewed shortly, and then the modern diffusion of products under the information propagation using multiagent simulation is discussed. The remarkable phenomena like “Winner-Takes-All” and “Chasm” can be observed, and one product marketing strategy is also proposed.

scholarly journals Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information Propagation in Hierarchical Modular Networks

2020 ◽  
Vol 10 (4) ◽  
pp. 228
Author(s):  
Rodrigo F. O. Pena ◽  
Vinicius Lima ◽  
Renan O. Shimoura ◽  
João Paulo Novato ◽  
Antonio C. Roque
Keyword(s):  
Network Structure ◽  
Network Models ◽  
Population Level ◽  
Synaptic Strength ◽  
Network Size ◽  
Strength Parameter ◽  
Information Propagation ◽  
Topological Features ◽  
Maximum Value ◽  
Parameter Ranges

In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects: (i) the increase of autocorrelations among individual neurons and (ii) the increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.

Highly fault-tolerant communication network models

1989 ◽  
Vol 36 (1) ◽  
pp. 23-30 ◽  
Author(s):  
J. Opatrny ◽  
N. Srinivasan ◽  
V.S. Alagar
Keyword(s):  
Communication Network ◽  
Fault Tolerant ◽  
Network Models

scholarly journals Networks: A

2021 ◽  
pp. 71-92
Author(s):  
Jean Walrand
Keyword(s):  
Social Networks ◽  
Communication Network ◽  
Social Network ◽  
Queueing Networks ◽  
Classical Result ◽  
Network Models ◽  
Product Form ◽  
The Internet ◽  
Free Products ◽  
Exchange Information

AbstractSocial networks connect people and enable them to exchange information. News and rumors spread through these networks. We explore models of such propagations. The technology behind social networks is the internet where packets travel from queue to queue. We explain some key results about queueing networks.Section 5.1 explores a model of how rumors spread in a social network. Epidemiologists use similar models to study the spread of viruses. Section 5.2 explains the cascade of choices in a social network where one person’s choice is influenced by those of people she knows. Section 5.3 shows how seeding the market with advertising or free products affects adoptions. Section 5.4 studies a model of how media can influence the eventual consensus in a social network. Section 5.5 explores the randomness of the consensus in a group. Sections 5.6 and 5.7 present a different class of network models where customers queue for service. Section 5.6 studies a single queue and Sect. 5.7 analyzes a network of queues. Section 5.8 explains a classical optimization problem in a communication network: how to choose the capacities of different links. Section 5.9 discusses the suitability of queueing networks as models of the internet. Section 5.10 presents a classical result about a class of queueing networks known as product-form networks.

scholarly journals Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 240
Author(s):  
Fang Cao ◽  
Cheng Li ◽  
Zhiyong Xu
Keyword(s):  
Communication Network ◽  
Network Models ◽  
Target Area ◽  
Communication Model ◽  
Network Coverage ◽  
Work Patterns ◽  
Polygon Area ◽  
Ultraviolet Communication ◽  
Arbitrary Polygon ◽  
Communication Radius

Given that the current ultraviolet (UV) networking model is established in a regular circular area, this article studies the coverage of a UV non-line-of-sight (NLOS) communication network creatively in the arbitrary polygon area. In this paper, the UV communication model and the basic concepts of network coverage are introduced first. Then the influence parameters of the UV node communication radius are studied, and the changes of the communication radius under different work patterns are analyzed. Finally, the coverage of the square target area is simulated under different communication parameters (transmitted power, data rate and node density). The results illustrate that the smaller the transceiver elevation angles are, the better the network coverage performance is. Additionally, we numerically compare the UV network models of polygonal and circular regions, which can be used as a reference for actual networking.

scholarly journals DESNN Algorithm For Communication Network Intrusion Detection

2021 ◽  
Author(s):  
Fulai Liu ◽  
Jialiang Xu ◽  
Lijie Zhang ◽  
Ruiyan Du ◽  
Zhibo Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Intrusion Detection ◽  
Network Models ◽  
Neural Network Models ◽  
Evolutionary Mechanism ◽  
Performance Loss ◽  
Network Intrusion ◽  
Ensemble Neural Network ◽  
Pruning Rule

Abstract Intrusion detection is a crucial technology in the communication network security field. In this paper, a dynamic evolutionary sparse neural network (DESNN) is proposed for intrusion detection, named as DESNN algorithm. Firstly, an ensemble neural network model is constructed, which is processed by a dynamic pruning rule and further divided into advantage subnetworks and disadvantage subnetworks. The dynamic pruning rule can effectively reduce the subnetworks weight parameters, thereby increasing the speed of the subnetworks intrusion detection. Then considering the subnetworks performance loss caused by the dynamic pruning rule, a novel evolutionary mechanism is proposed to optimize the training process of the disadvantage subnetworks. The weight of the disadvantage subnetworks approach the weight of the advantage subnetworks by the evolutionary mechanism, such that the performance of the ensemble neural network can be improved. Finally, an optimal subnetwork is selected from the ensemble neural network, which is used to detect multiple types of intrusion. Experiments show that the proposed DESNN algorithm improves intrusion detection speed without causing significant performance loss compare with other fully-connected neural network models.

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