197 research outputs found

    Influences of degree inhomogeneity on average path length and random walks in disassortative scale-free networks

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    Various real-life networks exhibit degree correlations and heterogeneous structure, with the latter being characterized by power-law degree distribution P(k)kγP(k)\sim k^{-\gamma}, where the degree exponent γ\gamma describes the extent of heterogeneity. In this paper, we study analytically the average path length (APL) of and random walks (RWs) on a family of deterministic networks, recursive scale-free trees (RSFTs), with negative degree correlations and various γ(2,1+ln3ln2]\gamma \in (2,1+\frac{\ln 3}{\ln 2}], with an aim to explore the impacts of structure heterogeneity on APL and RWs. We show that the degree exponent γ\gamma has no effect on APL dd of RSFTs: In the full range of γ\gamma, dd behaves as a logarithmic scaling with the number of network nodes NN (i.e. dlnNd \sim \ln N), which is in sharp contrast to the well-known double logarithmic scaling (dlnlnNd \sim \ln \ln N) previously obtained for uncorrelated scale-free networks with 2γ<32 \leq \gamma <3. In addition, we present that some scaling efficiency exponents of random walks are reliant on degree exponent γ\gamma.Comment: The definitive verion published in Journal of Mathematical Physic

    An alternative approach to determining average distance in a class of scale-free modular networks

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    Various real-life networks of current interest are simultaneously scale-free and modular. Here we study analytically the average distance in a class of deterministically growing scale-free modular networks. By virtue of the recursive relations derived from the self-similar structure of the networks, we compute rigorously this important quantity, obtaining an explicit closed-form solution, which recovers the previous result and is corroborated by extensive numerical calculations. The obtained exact expression shows that the average distance scales logarithmically with the number of nodes in the networks, indicating an existence of small-world behavior. We present that this small-world phenomenon comes from the peculiar architecture of the network family.Comment: Submitted for publicactio