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arxiv: cond-mat/0503598 · v1 · pith:EHFDXBX5new · submitted 2005-03-24 · ❄️ cond-mat.dis-nn

Scale-Free Networks Emerging from Weighted Random Graphs

classification ❄️ cond-mat.dis-nn
keywords scale-freepercolationrandomgraphslambdanetworkclusterslinks
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We study Erd\"{o}s-R\'enyi random graphs with random weights associated with each link. We generate a new ``Supernode network'' by merging all nodes connected by links having weights below the percolation threshold (percolation clusters) into a single node. We show that this network is scale-free, i.e., the degree distribution is $P(k)\sim k^{-\lambda}$ with $\lambda=2.5$. Our results imply that the minimum spanning tree (MST) in random graphs is composed of percolation clusters, which are interconnected by a set of links that create a scale-free tree with $\lambda=2.5$. We show that optimization causes the percolation threshold to emerge spontaneously, thus creating naturally a scale-free ``supernode network''. We discuss the possibility that this phenomenon is related to the evolution of several real world scale-free networks.

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