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arxiv: cond-mat/0411160 · v2 · submitted 2004-11-05 · ❄️ cond-mat.dis-nn · cond-mat.stat-mech

Universal scaling of distances in complex networks

classification ❄️ cond-mat.dis-nn cond-mat.stat-mech
keywords networksscalingdistancesmeanuniversalappearanceautonomousbarabasi-albert
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Universal scaling of distances between vertices of Erdos-Renyi random graphs, scale-free Barabasi-Albert models, science collaboration networks, biological networks, Internet Autonomous Systems and public transport networks are observed. A mean distance between two nodes of degrees k_i and k_j equals to <l_{ij}>=A-B log(k_i k_j). The scaling is valid over several decades. A simple theory for the appearance of this scaling is presented. Parameters A and B depend on the mean value of a node degree <k>_nn calculated for the nearest neighbors and on network clustering coefficients.

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