22 research outputs found
The Poset of Hypergraph Quasirandomness
Chung and Graham began the systematic study of k-uniform hypergraph
quasirandom properties soon after the foundational results of Thomason and
Chung-Graham-Wilson on quasirandom graphs. One feature that became apparent in
the early work on k-uniform hypergraph quasirandomness is that properties that
are equivalent for graphs are not equivalent for hypergraphs, and thus
hypergraphs enjoy a variety of inequivalent quasirandom properties. In the past
two decades, there has been an intensive study of these disparate notions of
quasirandomness for hypergraphs, and an open problem that has emerged is to
determine the relationship between them.
Our main result is to determine the poset of implications between these
quasirandom properties. This answers a recent question of Chung and continues a
project begun by Chung and Graham in their first paper on hypergraph
quasirandomness in the early 1990's.Comment: 43 pages, 1 figur
Eigenvalues of Non-Regular Linear-Quasirandom Hypergraphs
Chung, Graham, and Wilson proved that a graph is quasirandom if and only if
there is a large gap between its first and second largest eigenvalue. Recently,
the authors extended this characterization to k-uniform hypergraphs, but only
for the so-called coregular k-uniform hypergraphs. In this paper, we extend
this characterization to all k-uniform hypergraphs, not just the coregular
ones. Specifically, we prove that if a k-uniform hypergraph satisfies the
correct count of a specially defined four-cycle, then there is a gap between
its first and second largest eigenvalue.Comment: 15 pages. (this paper was originally part of an old version of
arXiv:1208.4863
Quasirandomness in hypergraphs
An -vertex graph of edge density is considered to be quasirandom
if it shares several important properties with the random graph . A
well-known theorem of Chung, Graham and Wilson states that many such `typical'
properties are asymptotically equivalent and, thus, a graph possessing one
such property automatically satisfies the others.
In recent years, work in this area has focused on uncovering more quasirandom
graph properties and on extending the known results to other discrete
structures. In the context of hypergraphs, however, one may consider several
different notions of quasirandomness. A complete description of these notions
has been provided recently by Towsner, who proved several central equivalences
using an analytic framework. We give short and purely combinatorial proofs of
the main equivalences in Towsner's result.Comment: 19 page
Hamilton cycles in quasirandom hypergraphs
We show that, for a natural notion of quasirandomness in -uniform
hypergraphs, any quasirandom -uniform hypergraph on vertices with
constant edge density and minimum vertex degree contains a
loose Hamilton cycle. We also give a construction to show that a -uniform
hypergraph satisfying these conditions need not contain a Hamilton -cycle
if divides . The remaining values of form an interesting
open question.Comment: 18 pages. Accepted for publication in Random Structures & Algorithm
Perfect Packings in Quasirandom Hypergraphs II
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 -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
How unproportional must a graph be?
Let be the maximum over all -vertex graphs of by how much
the number of induced copies of in differs from its expectation in the
binomial random graph with the same number of vertices as and with edge
probability . This may be viewed as a measure of how close is to being
-quasirandom. For a positive integer and , let be the
distance from to the nearest integer. Our main result is that,
for fixed and for large, the minimum of over -vertex
graphs has order of magnitude
provided that