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Research on a Scale-Free Network with Tunable Clustering Coefficient

Qin Lin Yang, Lulong Lan, Jianhui Zhang

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Abstract

Network topology plays an important role in design and optimization of routing protocols and network algorithms. Based on the characteristics of real networks and local world phenomenon, this paper proposes a evolving scale-free network with tunable clustering coefficient(SFTC) by introducing triad formation, which dynamically simulates the adding and deleting of nodes or edges, and the evolvements inside or outside the local world. The evaluation of C++ and Matlab not only support the analytical calculation of model's degree distribution, but also indicates that the clustering coefficient can be tuned to be a preferable value and the SFTC model totally reproduces the properties of real network.

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What this paper is about

Network topology plays an important role in design and optimization of routing protocols and network algorithms. Based on the characteristics of real networks and local world phenomenon, this paper proposes a evolving scale-free network with tunable clustering coefficient(SFTC) by introducing triad formation, which dynamically simulates the adding and deleting of nodes or edges, and the evolvements inside or outside the local world. The evaluation of C++ and Matlab not only support the analytical calculation of model's degree distribution, but also indicates that the clustering coefficient can be tuned to be a preferable value and the SFTC model totally reproduces the properties of real network.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Network topology plays an important role in design and optimization of routing protocols and network algorithms. Based on the characteristics of real networks and local world phenomenon, this paper proposes a evolving scale-free network with tunable clustering coefficient(SFTC) by introducing triad formation, which dynamically simulates the adding and deleting of nodes or edges, and the evolvements inside or outside the local world. The evaluation of C++ and Matlab not only support the analytical calculation of model's degree distribution, but also indicates that the clustering coefficient can be tuned to be a preferable value and the SFTC model totally reproduces the properties of real network.

Key concepts: Clustering coefficient, Cluster analysis, Computer science, Scale-free network, Complex network, Routing (electronic design automation), Scale (ratio), MATLAB

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