An insertion–deletion–compensation model with Poisson process for scale-free networks

Li, Jinqiang; Zhou, Shuming; Li, Xuequn; Li, Xiao-Wang

doi:10.1016/j.future.2017.04.011

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…At the same time, the scale-free network models were developed, which leverage the mechanism of preferential connection, redirection or copy to interpret the power-law property of the node degree distribution. In recent years, following the above typical models, new mechanisms represented by weight [6][7][8][9], local world [10,11], nonlinear growth [11][12][13][14], location information [15][16][17][18], popularity and homophily [19][20][21], and triangle closure [22][23][24][25][26] are used to construct network evolution models.…”

Section: Introductionmentioning

confidence: 99%

Modeling Complex Networks Based on Deep Reinforcement Learning

et al. 2022

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The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

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Section: Introductionmentioning

confidence: 99%

Modeling Complex Networks Based on Deep Reinforcement Learning

et al. 2022

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“…The preferential attachment function has been expanded from the initial case, where it was linearly proportional to the size of the nodes' degree, to different preference types [9,10]. In addition to these improvements, other evolutionary mechanisms (such as replication, intermediate agents, etc) have also been explored [11][12][13][14][15][16][17]. During the development of the network degree distribution analysis methods of the BA model and its extended models, many extended evolution models were also proposed.…”

Section: Introductionmentioning

confidence: 99%

An extended scale-free network evolution model based on global coupling motif embedding

et al. 2021

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The BA scale-free network evolution model assumes that one node enters at every unit time, which does not adequately reflect team entries that usually occur during the evolution of many practical networks, i.e. the phenomenon of motif embedment. Unfortunately, there are no specific studies on how the motif embedment mechanism affects the degree distribution of networks. In order to solve this problem, an extended scale-free network evolution model with global coupling motif embedment and with the motif size obeying an arbitrary discrete probability distribution (called the MEEBA model for short) is formulated with the help of the ‘motif’ concept. Using the Markov chain method, the accurate analytical expression of the network degree distribution of the MEEBA model is obtained and the correctness of the equation is verified through comparisons with numerical simulation results. The study results show that the right tail of the degree distribution of the MEEBA model still has a power-law behavior, while its left tail reflects the horse-head-like shapes of the degree distribution of many real networks. Furthermore, the power-law exponent and the horse-head shapes are both related to the distribution of the motif size. When the motif size follows a one-point distribution, the network degree distribution of the MEEBA model degenerates into a two-parameter Waring-like distribution and its horse-head shapes disappear. In particular, if the expectation of the one-point distribution is 1, the MEEBA model further degenerates to the BA model. Finally, the practicality and effectiveness of the MEEBA model are verified through a case study.

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A Study on Scale Free Social Network Evolution Model with Degree Exponent < 2

Tang

2020

J Syst Sci Complex

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An insertion–deletion–compensation model with Poisson process for scale-free networks

Cited by 6 publications

References 25 publications

Modeling Complex Networks Based on Deep Reinforcement Learning

Modeling Complex Networks Based on Deep Reinforcement Learning

An extended scale-free network evolution model based on global coupling motif embedding

A Study on Scale Free Social Network Evolution Model with Degree Exponent < 2

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