scholarly journals Immunization of Cooperative Spreading Dynamics on Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jun Wang ◽  
Shi-Min Cai ◽  
Tao Zhou
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Percolation Theory ◽  
Large Degree ◽  
Transmission Probability ◽  
Final State ◽  
The Monte Carlo Method ◽  
Spreading Dynamics ◽  
Opposite Situation ◽  
The Cost

Cooperative spreading dynamics on complex networks is a hot topic in the field of network science. In this paper, we propose a strategy to immunize some nodes based on their degrees. The immunized nodes disable the synergistic effect of cooperative spreading dynamics. We also develop a generalized percolation theory to study the final state of the spreading dynamics. By using the Monte Carlo method, numerical simulations reveal that immunizing nodes with a large degree cannot always be beneficial for containing cooperative spreading. For small values of transmission probability, immunizing hubs can suppress the spreading, while the opposite situation happens for large values of transmission probability. Furthermore, numerical simulations show that immunizing hubs increase the cost of the system. Finally, all numerical simulations can be well predicted by the generalized percolation theory.

Cooperate epidemic spreading on multiplex networks with heterogeneous population

2020 ◽  
Vol 31 (09) ◽  
pp. 2050132
Author(s):  
Tianqiao Zhang ◽  
Yang Zhang ◽  
Jinming Ma ◽  
Junliang Chen ◽  
Xuzhen Zhu
Keyword(s):  
Phase Transition ◽  
Numerical Simulations ◽  
Percolation Theory ◽  
Bond Percolation ◽  
Epidemic Spreading ◽  
Final State ◽  
Multiplex Networks ◽  
Spreading Dynamics ◽  
Discontinuous Phase ◽  
Outbreak Size

Cooperate epidemic spreading dynamics has attracted much attention from the field of network science. In this paper, we study the cooperate epidemic spreading dynamics on multiplex networks with heterogeneous populations, which induces the heterogeneous coinfection susceptibility. We propose a spreading model to describe the evolution mechanisms. To predict the final state of the epidemic outbreak size, a generalized bond percolation theory is suggested. Through numerical simulations and theoretical analyses, we find that the system exhibits a discontinuous phase transition for large average and small variance of the distribution of coinfection susceptibility on ER–ER multiplex networks, while the phase transition is continuous on SF–SF networks. In addition, the final outbreak size increases with the average coinfection susceptibility and decreases with the variance of the coinfection susceptibility. Our suggested bond percolation theory can well predict the numerical simulations.

scholarly journals Information Spreading on Memory Activity-Driven Temporal Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Linfeng Zhong ◽  
Yu Bai ◽  
Changjiang Liu ◽  
Juan Du ◽  
Weijun Pan
Keyword(s):  
Information Transmission ◽  
Memory Effect ◽  
Real Data ◽  
Simulation Method ◽  
Transmission Probability ◽  
Temporal Networks ◽  
Spreading Dynamics ◽  
Opposite Situation ◽  
Information Spreading ◽  
Markovian Approach

Information spreading dynamics on temporal networks have attracted significant attention in the field of network science. Extensive real-data analyses revealed that network memory widely exists in the temporal network. This paper proposes a mathematical model to describe the information spreading dynamics with the network memory effect. We develop a Markovian approach to describe the model. Using the Monte Carlo simulation method, we find that network memory may suppress and promote the information spreading dynamics, which depends on the degree heterogeneity and fraction of bigots. The network memory effect suppresses the information spreading for small information transmission probability. The opposite situation happens for large value of information transmission probability. Moreover, network memory effect may benefit the information spreading, which depends on the degree heterogeneity of the activity-driven network. Our results presented in this paper help us understand the spreading dynamics on temporal networks.

scholarly journals Phase diagrams of interacting spreading dynamics in complex networks

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Liming Pan ◽  
Dan Yang ◽  
Wei Wang ◽  
Shimin Cai ◽  
Tao Zhou ◽  
...  
Keyword(s):  
Complex Networks ◽  
Phase Diagrams ◽  
Spreading Dynamics

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

Fire spreading experiments on heterogeneous fuel beds. Applications of percolation theory

2000 ◽  
Vol 30 (8) ◽  
pp. 1318-1328 ◽  
Author(s):  
Jean Nahmias ◽  
Hervé Téphany ◽  
José Duarte ◽  
Sophie Letaconnoux
Keyword(s):  
Experimental Work ◽  
Percolation Theory ◽  
Dynamic Behaviour ◽  
Fire Spread ◽  
Directed Percolation ◽  
Final State ◽  
Critical Thresholds ◽  
Scale Models ◽  
Different Types ◽  
Fire Spreading

In the experimental work presented here, fire spread was studied through various laboratory and full-scale models containing different types of combustible and noncombustible materials. We have examined the dynamic behaviour of the flame front and the final state (after extinction) on randomly created heterogeneous zones, both with and without wind. The principal conclusion is that critical thresholds exist, for the ratio between combustible and noncombustible parts, at the transition between nonpropagation and propagation of the fire. This result is common to all types of spreading (with or without wind). The values of the critical thresholds in the nonwind-driven experiments are those of the percolation theory. The critical exponent, obtained for wind-driven experiments, is in accordance with current values suggested by the directed percolation approach.

scholarly journals Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Haipeng Peng ◽  
Lixiang Li ◽  
Jürgen Kurths ◽  
Shudong Li ◽  
Yixian Yang
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Complex Network ◽  
Parameter Optimization ◽  
Ant Algorithm ◽  
Structure Parameters ◽  
Topology Identification ◽  
Unknown Structure ◽  
Swarm Algorithm ◽  
Scientific Fields

Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.

Numerical Simulation of Dynamics of a Spar Type Floating Wind Turbine and Comparison With Laboratory Measurements

2016 ◽  
Author(s):  
Hyunseong Min ◽  
Cheng Peng ◽  
Fei Duan ◽  
Zhiqiang Hu ◽  
Jun Zhang
Keyword(s):  
Numerical Simulation ◽  
Wind Speed ◽  
Numerical Simulations ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Deep Water ◽  
Offshore Wind ◽  
Wind Loads ◽  
Floating Offshore Wind Turbines ◽  
The Cost

Wind turbines are popular for harnessing wind energy. Floating offshore wind turbines (FOWT) installed in relatively deep water may have advantages over their on-land or shallow-water cousins because winds over deep water are usually steadier and stronger. As the size of wind turbines becomes larger and larger for reducing the cost per kilowatt, it could bring installation and operation risks in the deep water due to the lack of track records. Thus, together with laboratory tests, numerical simulations of dynamics of FOWT are desirable to reduce the probability of failure. In this study, COUPLE-FAST was initially employed for the numerical simulations of the OC3-HYWIND, a spar type platform equipped with the 5-MW baseline wind turbine proposed by National Renewable Energy Laboratory (NREL). The model tests were conducted at the Deepwater Offshore Basin in Shanghai Jiao Tong University (SJTU) with a 1:50 Froude scaling [1]. In comparison of the simulation using COUPLE-FAST with the corresponding measurements, it was found that the predicted motions were in general significantly smaller than the related measurements. The main reason is that the wind loads predicted by FAST were well below the related measurements. Large discrepancies are expected because the prototype and laboratory wind loads do not follow Froude number similarity although the wind speed was increased (or decreased) in the tests such that the mean surge wind force matched that predicted by FAST at the nominal wind speed (Froude similarity) in the cases of a land wind turbine [1]. Therefore, an alternative numerical simulation was made by directly inputting the measured wind loads to COUPLE instead of the ones predicted by FAST. The related simulated results are much improved and in satisfactory agreement with the measurements.

scholarly journals A Study of an EOQ Model under Lock Fuzzy Environment

Mathematics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 75 ◽  
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Sankar Prasad Mondal
Keyword(s):  
Inventory Management ◽  
Numerical Study ◽  
Management Problem ◽  
Final State ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Computer Based ◽  
The Cost ◽  
Cost Vector

The present article was developed for the economic order quantity (EOQ) inventory model under daytime, non-random, uncertain demand. In any inventory management problem, several parameters are involved that are basically flexible in nature with the progress of time. This model can be split into three different sub-models, assuming the demand rate and the cost vector associated with the model are non-randomly uncertain (i.e., fuzzy), and these may include some of the retained learning experiences of the decision-maker (DM). However, the DM has the option of revising his/her decision through the application of the appropriate key vector of the fuzzy locks in their final state. The basic novelty of the present model is that it includes a computer-based decision‐making process involving flowchart algorithms that are able to identify and update the key vectors automatically. The numerical study indicates that when all parameters are assumed to be fuzzy, the double keys of the fuzzy lock provide a more accurate optimum than other methods. Sensitivity analysis and graphical illustrations are made for better justification of the model.

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