70 research outputs found

    Vertex labeling and routing in expanded Apollonian networks

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    We present a family of networks, expanded deterministic Apollonian networks, which are a generalization of the Apollonian networks and are simultaneously scale-free, small-world, and highly clustered. We introduce a labeling of their vertices that allows to determine a shortest path routing between any two vertices of the network based only on the labels.Comment: 16 pages, 2 figure

    Long paths in random Apollonian networks

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    We consider the length L(n)L(n) of the longest path in a randomly generated Apollonian Network (ApN) An{\cal A}_n. We show that w.h.p. L(n)nelogcnL(n)\leq ne^{-\log^cn} for any constant c<2/3c<2/3

    Degrees and distances in random and evolving Apollonian networks

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    This paper studies Random and Evolving Apollonian networks (RANs and EANs), in d dimension for any d>=2, i.e. dynamically evolving random d dimensional simplices looked as graphs inside an initial d-dimensional simplex. We determine the limiting degree distribution in RANs and show that it follows a power law tail with exponent tau=(2d-1)/(d-1). We further show that the degree distribution in EANs converges to the same degree distribution if the simplex-occupation parameter in the n-th step of the dynamics is q_n->0 and sum_{n=0}^infty q_n =infty. This result gives a rigorous proof for the conjecture of Zhang et al. that EANs tend to show similar behavior as RANs once the occupation parameter q->0. We also determine the asymptotic behavior of shortest paths in RANs and EANs for arbitrary d dimensions. For RANs we show that the shortest path between two uniformly chosen vertices (typical distance), the flooding time of a uniformly picked vertex and the diameter of the graph after n steps all scale as constant times log n. We determine the constants for all three cases and prove a central limit theorem for the typical distances. We prove a similar CLT for typical distances in EANs

    Exact analytical solution of average path length for Apollonian networks

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    The exact formula for the average path length of Apollonian networks is found. With the help of recursion relations derived from the self-similar structure, we obtain the exact solution of average path length, dˉt\bar{d}_t, for Apollonian networks. In contrast to the well-known numerical result dˉt(lnNt)3/4\bar{d}_t \propto (\ln N_t)^{3/4} [Phys. Rev. Lett. \textbf{94}, 018702 (2005)], our rigorous solution shows that the average path length grows logarithmically as dˉtlnNt\bar{d}_t \propto \ln N_t in the infinite limit of network size NtN_t. The extensive numerical calculations completely agree with our closed-form solution.Comment: 8 pages, 4 figure

    Characterizing the network topology of the energy landscapes of atomic clusters

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    By dividing potential energy landscapes into basins of attractions surrounding minima and linking those basins that are connected by transition state valleys, a network description of energy landscapes naturally arises. These networks are characterized in detail for a series of small Lennard-Jones clusters and show behaviour characteristic of small-world and scale-free networks. However, unlike many such networks, this topology cannot reflect the rules governing the dynamics of network growth, because they are static spatial networks. Instead, the heterogeneity in the networks stems from differences in the potential energy of the minima, and hence the hyperareas of their associated basins of attraction. The low-energy minima with large basins of attraction act as hubs in the network.Comparisons to randomized networks with the same degree distribution reveals structuring in the networks that reflects their spatial embedding.Comment: 14 pages, 11 figure

    Vertex labeling and routing in expanded Apollonian networks

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    We present a family of networks, expanded deterministic Apollonian networks, which are a generalization of the Apollonian networks and are simultaneously scale-free, small-world, and highly clustered. We introduce a labeling of their nodes that allows one to determine a shortest path routing between any two nodes of the network based only on the labels
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