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      The degree distribution and the number of edges between nodes of given degrees in the Buckley-Osthus model of a random web graph

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          Abstract

          In this paper, we study some important statistics of the random graph in the Buckley-Osthus model. This model is a modification of the well-known Bollob\'as-Riordan model. We denote the number of nodes by t, the so-called initial attractiveness of a node by a. First, we find a new asymptotic formula for the expectation of the number R(d,t) of nodes of a given degree d in a graph in this model. Such a formula is known for positive integer values of a and d \le t^{1/100(a+1)}. Both restrictions are unsatisfactory from theoretical and practical points of view. We completely remove them. Then we calculate the covariances between any two quantities R(d_1,t), R(d_2,t), and using the second moment method we show that R(d,t) is tightly concentrated around its mean for every possible values of d and t. Furthermore, we study a more complicated statistic of the web graph: X(d_1,d_2,t) is the total number of edges between nodes whose degrees are equal to d_1 and d_2 respectively. We also find an asymptotic formula for the expectation of X(d_1,d_2,t) and prove a tight concentration result. Again, we do not impose any substantial restrictions on the values of d_1, d_2, and t.

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Weighted sums of certain dependent random variables

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              Structure of Growing Networks: Exact Solution of the Barabasi--Albert's Model

              We generalize the Barab\'{a}si--Albert's model of growing networks accounting for initial properties of sites and find exactly the distribution of connectivities of the network \(P(q)\) and the averaged connectivity \(\bar{q}(s,t)\) of a site \(s\) in the instant \(t\) (one site is added per unit of time). At long times \(P(q) \sim q^{-\gamma}\) at \(q \to \infty\) and \(\bar{q}(s,t) \sim (s/t)^{-\beta}\) at \(s/t \to 0\), where the exponent \(\gamma\) varies from 2 to \(\infty\) depending on the initial attractiveness of sites. We show that the relation \(\beta(\gamma-1)=1\) between the exponents is universal.
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                Author and article information

                Journal
                19 August 2011
                Article
                1108.4054
                ab001e22-13ca-4c89-9098-73bd4333f305

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                20 pages
                math.PR math.CO

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