2,756 research outputs found
Random subcube intersection graphs I: cliques and covering
We study random subcube intersection graphs, that is, graphs obtained by
selecting a random collection of subcubes of a fixed hypercube to serve
as the vertices of the graph, and setting an edge between a pair of subcubes if
their intersection is non-empty. Our motivation for considering such graphs is
to model `random compatibility' between vertices in a large network. For both
of the models considered in this paper, we determine the thresholds for
covering the underlying hypercube and for the appearance of s-cliques. In
addition we pose some open problems.Comment: 38 pages, 1 figur
Short proofs of some extremal results
We prove several results from different areas of extremal combinatorics,
giving complete or partial solutions to a number of open problems. These
results, coming from areas such as extremal graph theory, Ramsey theory and
additive combinatorics, have been collected together because in each case the
relevant proofs are quite short.Comment: 19 page
Learning multifractal structure in large networks
Generating random graphs to model networks has a rich history. In this paper,
we analyze and improve upon the multifractal network generator (MFNG)
introduced by Palla et al. We provide a new result on the probability of
subgraphs existing in graphs generated with MFNG. From this result it follows
that we can quickly compute moments of an important set of graph properties,
such as the expected number of edges, stars, and cliques. Specifically, we show
how to compute these moments in time complexity independent of the size of the
graph and the number of recursive levels in the generative model. We leverage
this theory to a new method of moments algorithm for fitting large networks to
MFNG. Empirically, this new approach effectively simulates properties of
several social and information networks. In terms of matching subgraph counts,
our method outperforms similar algorithms used with the Stochastic Kronecker
Graph model. Furthermore, we present a fast approximation algorithm to generate
graph instances following the multi- fractal structure. The approximation
scheme is an improvement over previous methods, which ran in time complexity
quadratic in the number of vertices. Combined, our method of moments and fast
sampling scheme provide the first scalable framework for effectively modeling
large networks with MFNG
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