10 research outputs found

    Perfect Packings in Quasirandom Hypergraphs II

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    For each of the notions of hypergraph quasirandomness that have been studied, we identify a large class of hypergraphs F so that every quasirandom hypergraph H admits a perfect F-packing. An informal statement of a special case of our general result for 3-uniform hypergraphs is as follows. Fix an integer r >= 4 and 0<p<1. Suppose that H is an n-vertex triple system with r|n and the following two properties: * for every graph G with V(G)=V(H), at least p proportion of the triangles in G are also edges of H, * for every vertex x of H, the link graph of x is a quasirandom graph with density at least p. Then H has a perfect Kr(3)K_r^{(3)}-packing. Moreover, we show that neither hypotheses above can be weakened, so in this sense our result is tight. A similar conclusion for this special case can be proved by Keevash's hypergraph blowup lemma, with a slightly stronger hypothesis on H.Comment: 17 page

    Quasi-random oriented graphs

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    We show that a number of conditions on oriented graphs, all of which are satisfied with high probability by randomly oriented graphs, are equivalent. These equivalences are similar to those given by Chung, Graham and Wilson in the case of unoriented graphs, and by Chung and Graham in the case of tournaments. Indeed, our main theorem extends to the case of a general underlying graph G the main result of Chung and Graham which corresponds to the case that G is complete. One interesting aspect of these results is that exactly two of the four orientations of a four-cycle can be used for a quasi-randomness condition, i.e., if the number of appearances they make in D is close to the expected number in a random orientation of the same underlying graph, then the same is true for every small oriented graph HComment: 11 page

    DENSE GRAPH LIMITS AND APPLICATIONS

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    In recent years, there has been a growing need to understand large networks and to devise effective strategies to analyze them. In this dissertation, our main objectives are to understand various structural properties of large networks under suitable general framework and develop general techniques to analyze important network models arising from applied fields of study. In the first part of this dissertation, we investigate properties of large networks that satisfy certain local conditions. In particular, we show that if the number of neighbors of each vertex and co-neighbors of each pair of vertices satises certain conditions then the number of copies of moderately large subgraphs is approximately same as that of an Erdos-Renyi random graph with appropriate edge density. We apply our results to different graph ensembles including exponential random graph models (ERGMs), thresholded graphs from high-dimensional correlation networks, Erdos-Renyi random graphs conditioned on large cliques and random d-regular graphs. In the second part of this dissertation, we study models of weighted exponential random graphs in the large network limit. These models have recently been proposed to model weighted network data arising from a host of applications including socio-econometric data such as migration flows and neuroscience. We derive limiting results for the structure of these models as the number of nodes goes to infinity. We also derive sucient conditions for continuity of functionals in the specification of the model including conditions on nodal covariates. Finally, we study site percolation on a class of non-regular graphs satisfying some mild assumptions on the number of neighbors of each vertex and co-neighbors of each pair of vertices. We show that there is a sharp phase transition (in site percolation) for the class of graphs under consideration and that in the supercritical regime the giant component is unique.Doctor of Philosoph
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