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2020•Journal of Physics Conference SeriesOpen access

An Improved B-A Model for Scale-free Network Evolution Model

Hongyan Wei, Jiangong Wang, Tianqi Wang

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Abstract

Abstract Scale free network can describe complex network very well. Barabasi and Albert proposed B-A model, which reveals the nature of many complex phenomena in the real world. The generated network degree distribution satisfies power-law distribution. In this paper, two shortcomings of the model are found through careful study, and an improved scale-free network evolution model is proposed. The experimental data show that the improved model has significant scale-free characteristics and can better reflect some characteristics of complex networks.

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What this paper is about

Abstract Scale free network can describe complex network very well. Barabasi and Albert proposed B-A model, which reveals the nature of many complex phenomena in the real world. The generated network degree distribution satisfies power-law distribution. In this paper, two shortcomings of the model are found through careful study, and an improved scale-free network evolution model is proposed. The experimental data show that the improved model has significant scale-free characteristics and can better reflect some characteristics of complex networks.

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Available abstract

Abstract Scale free network can describe complex network very well. Barabasi and Albert proposed B-A model, which reveals the nature of many complex phenomena in the real world. The generated network degree distribution satisfies power-law distribution. In this paper, two shortcomings of the model are found through careful study, and an improved scale-free network evolution model is proposed. The experimental data show that the improved model has significant scale-free characteristics and can better reflect some characteristics of complex networks.

Key concepts: Scale-free network, Degree distribution, Preferential attachment, Network model, Complex network, Scale (ratio), Computer science, Network formation

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