1,718 research outputs found

    Creation and Growth of Components in a Random Hypergraph Process

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    Denote by an \ell-component a connected bb-uniform hypergraph with kk edges and k(b1)k(b-1) - \ell vertices. We prove that the expected number of creations of \ell-component during a random hypergraph process tends to 1 as \ell and bb tend to \infty with the total number of vertices nn such that =o(nb3)\ell = o(\sqrt[3]{\frac{n}{b}}). Under the same conditions, we also show that the expected number of vertices that ever belong to an \ell-component is approximately 121/3(b1)1/31/3n2/312^{1/3} (b-1)^{1/3} \ell^{1/3} n^{2/3}. As an immediate consequence, it follows that with high probability the largest \ell-component during the process is of size O((b1)1/31/3n2/3)O((b-1)^{1/3} \ell^{1/3} n^{2/3}). Our results give insight about the size of giant components inside the phase transition of random hypergraphs.Comment: R\'{e}sum\'{e} \'{e}tend

    Counting connected hypergraphs via the probabilistic method

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    In 1990 Bender, Canfield and McKay gave an asymptotic formula for the number of connected graphs on [n][n] with mm edges, whenever nn and the nullity mn+1m-n+1 tend to infinity. Asymptotic formulae for the number of connected rr-uniform hypergraphs on [n][n] with mm edges and so nullity t=(r1)mn+1t=(r-1)m-n+1 were proved by Karo\'nski and \L uczak for the case t=o(logn/loglogn)t=o(\log n/\log\log n), and Behrisch, Coja-Oghlan and Kang for t=Θ(n)t=\Theta(n). Here we prove such a formula for any r3r\ge 3 fixed, and any t=t(n)t=t(n) satisfying t=o(n)t=o(n) and tt\to\infty as nn\to\infty. This leaves open only the (much simpler) case t/nt/n\to\infty, which we will consider in future work. ( arXiv:1511.04739 ) Our approach is probabilistic. Let Hn,prH^r_{n,p} denote the random rr-uniform hypergraph on [n][n] in which each edge is present independently with probability pp. Let L1L_1 and M1M_1 be the numbers of vertices and edges in the largest component of Hn,prH^r_{n,p}. We prove a local limit theorem giving an asymptotic formula for the probability that L1L_1 and M1M_1 take any given pair of values within the `typical' range, for any p=p(n)p=p(n) in the supercritical regime, i.e., when p=p(n)=(1+ϵ(n))(r2)!nr+1p=p(n)=(1+\epsilon(n))(r-2)!n^{-r+1} where ϵ3n\epsilon^3n\to\infty and ϵ0\epsilon\to 0; our enumerative result then follows easily. Taking as a starting point the recent joint central limit theorem for L1L_1 and M1M_1, we use smoothing techniques to show that `nearby' pairs of values arise with about the same probability, leading to the local limit theorem. Behrisch et al used similar ideas in a very different way, that does not seem to work in our setting. Independently, Sato and Wormald have recently proved the special case r=3r=3, with an additional restriction on tt. They use complementary, more enumerative methods, which seem to have a more limited scope, but to give additional information when they do work.Comment: Expanded; asymptotics clarified - no significant mathematical changes. 67 pages (including appendix

    Woven Graph Codes: Asymptotic Performances and Examples

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    Constructions of woven graph codes based on constituent block and convolutional codes are studied. It is shown that within the random ensemble of such codes based on ss-partite, ss-uniform hypergraphs, where ss depends only on the code rate, there exist codes satisfying the Varshamov-Gilbert (VG) and the Costello lower bound on the minimum distance and the free distance, respectively. A connection between regular bipartite graphs and tailbiting codes is shown. Some examples of woven graph codes are presented. Among them an example of a rate Rwg=1/3R_{\rm wg}=1/3 woven graph code with dfree=32d_{\rm free}=32 based on Heawood's bipartite graph and containing n=7n=7 constituent rate Rc=2/3R^{c}=2/3 convolutional codes with overall constraint lengths νc=5\nu^{c}=5 is given. An encoding procedure for woven graph codes with complexity proportional to the number of constituent codes and their overall constraint length νc\nu^{c} is presented.Comment: Submitted to IEEE Trans. Inform. Theor
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