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In this paper, we propose an evolving random network. The model is a linear combination of preferential attachment model and uniform model. We show that scaling limit distribution of the number of leaves at time $n$ is approximated by normal distribution and the proportional degree sequence obeys power law. The branching structure and maximum degree are also discussed in this paper.
}, issn = {2707-8523}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/cmr/19004.html} }In this paper, we propose an evolving random network. The model is a linear combination of preferential attachment model and uniform model. We show that scaling limit distribution of the number of leaves at time $n$ is approximated by normal distribution and the proportional degree sequence obeys power law. The branching structure and maximum degree are also discussed in this paper.