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原文連結
論文資訊
- 類型：已發表論文
- 日期：2016-12-08
摘要
We introduce a growing 網絡 model, the copying model, in which a newnode attaches to a randomly selected target node and, in addition, independently to each of the neighbors of the target with copying probability p. When p < 1/2, this algorithm generates sparse 網絡s, in which the average node degree is finite. A power-law degree distribution also arises, with a nonuniversal exponent whose value is determined by a transcendental equation in p. In the sparse regime, the 網絡 is "normal," e. g., the relative fluctuations in the number of links are asymptotically negligible. For p >= 1/2, the emergent 網絡s are dense (the average degree increases with the number of nodes N), and they exhibit intriguing structural behaviors. In particular, the N dependence of the number of m cliques (complete subgraph
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