Random Acyclic Networks

被引:29
作者
Karrer, Brian [1 ]
Newman, M. E. J.
机构
[1] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA
基金
美国国家科学基金会;
关键词
D O I
10.1103/PhysRevLett.102.128701
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Directed acyclic graphs make up a fundamental class of networks that includes citation networks, food webs, and family trees, among others. Here we define a random graph model for directed acyclic graphs and give solutions for a number of the model's properties, including connection probabilities and component sizes, as well as a fast algorithm for simulating the model on a computer. We compare the predictions of the model to a real-world network of citations between physics papers and find surprisingly good agreement, suggesting that the structure of the real network may be quite well described by the random graph.
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页数:4
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