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

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

Selectivity-based spreading dynamics on complex networks

2008 ◽  
Vol 78 (2) ◽  
Author(s):  
Rui Yang ◽  
Liang Huang ◽  
Ying-Cheng Lai
Keyword(s):  
Complex Networks ◽  
Spreading Dynamics

scholarly journals Detecting hidden layers from spreading dynamics on complex networks

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
Łukasz G. Gajewski ◽  
Jan Chołoniewski ◽  
Mateusz Wilinski
Keyword(s):  
Complex Networks ◽  
Spreading Dynamics

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.

scholarly journals Impact of Immunization Strategies on the Dynamics of Social Contagions

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hao Peng ◽  
Wangxin Peng ◽  
Dandan Zhao ◽  
Zhaolong Hu ◽  
Jianmin Han ◽  
...  
Keyword(s):  
Complex Networks ◽  
Threshold Model ◽  
Network Heterogeneity ◽  
Immunization Strategies ◽  
Spreading Dynamics ◽  
Theoretical Predictions ◽  
Underlying Network ◽  
Edge Based ◽  
The Individual ◽  
The Impact

Immunization strategies on complex networks are effective methods to control the spreading dynamics on complex networks, which change the topology and connectivity of the underlying network, thereby affecting the dynamics process of propagation. Here, we use a non-Markovian threshold model to study the impact of immunization strategies on social contagions, in which the immune index greater than (or equal to) 0 corresponds to targeted (random) immunization, and when the immune index is less than 0, the probability of an individual being immunized is inversely related to the degree of the individual. A generalized edge-based compartmental theory is developed to analyze the dynamics of social contagions under immunization, and theoretical predictions are very consistent with simulation results. We find that increasing the immune index or increasing the immune ratio will reduce the final adoption size and increase the outbreak threshold, in other words, make the residual network after immunization not conducive to social contagions. Interestingly, enhancing the network heterogeneity is proved to help improve the immune efficiency of targeted immunization. Besides, the dependence of the outbreak threshold on the network heterogeneity is correlated with the immune ratio and immune index.

Identifying all-around nodes for spreading dynamics in complex networks

2012 ◽  
Vol 391 (15) ◽  
pp. 4012-4017 ◽  
Author(s):  
Bonan Hou ◽  
Yiping Yao ◽  
Dongsheng Liao
Keyword(s):  
Complex Networks ◽  
Spreading Dynamics
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