Results 121 to 130 of about 56,309 (276)
A Sharper Ramsey Theorem for Constrained Drawings
ABSTRACT Given a graph G and a collection C of subsets of R d indexed by the subsets of vertices of G, a constrained drawing of G is a drawing where each edge is drawn inside some set from C, in such a way that nonadjacent edges are drawn in sets with disjoint indices. In this paper we prove a Ramsey‐type result for such drawings.
Pavel Paták
wiley +1 more source
Weak hypergraph regularity and linear hypergraphs
AbstractWe consider conditions which allow the embedding of linear hypergraphs of fixed size. In particular, we prove that any k-uniform hypergraph H of positive uniform density contains all linear k-uniform hypergraphs of a given size. More precisely, we show that for all integers ℓ⩾k⩾2 and every d>0 there exists ϱ>0 for which the following holds: if ...
Vojtěch Rödl+3 more
openaire +2 more sources
Conformal Hypergraphs: Duality and Implications for the Upper Clique Transversal Problem
ABSTRACT Given a hypergraph ℋ, the dual hypergraph of ℋ is the hypergraph of all minimal transversals of ℋ. The dual hypergraph is always Sperner, that is, no hyperedge contains another. A special case of Sperner hypergraphs are the conformal Sperner hypergraphs, which correspond to the families of maximal cliques of graphs.
Endre Boros+3 more
wiley +1 more source
On Frankl and Furedi's conjecture for 3-uniform hypergraphs [PDF]
The Lagrangian of a hypergraph has been a useful tool in hypergraph extremal problems. In most applications, we need an upper bound for the Lagrangian of a hypergraph. Frankl and Furedi in \cite{FF} conjectured that the $r$-graph with $m$ edges formed by
Peng, Hao+3 more
core
Dynamic Hypergraph Neural Networks
In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model.
Jianwen Jiang+4 more
semanticscholar +1 more source
ABSTRACT The accurate and early detection of kidney stones is crucial for effective treatment and patient management. This study presents a hybrid machine learning approach combining Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) for the multi‐classification of kidney stones.
Setlhabi Letlhogonolo Rapelang+1 more
wiley +1 more source
Interval hypergraphs and D-interval hypergraphs
AbstractA hypergraph H = (V, E) is called an interval hypergraph if there exists a one-to-one function ƒ mapping the elements of V to points on the real line such that for each edge E, there is an interval I, containing the images of all elements of E, but not the images of any elements not in E1.
openaire +1 more source
AbstractWe investigate the following question: ‘Given an intersecting multi-hypergraph on n points, what fraction of edges must be covered by any of the best 2 points?’ (Here ‘best’ means that together they cover the most.) We call this M2(n). This is a special case of a question asked by Erdös and Gyárfás (1990) (they considered r-wise intersecting ...
Barry Guiduli, Zoltán Király
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Demonstration of hypergraph-state quantum information processing
Complex entangled states are the key resources for measurement-based quantum computations, which is realised by performing a sequence of measurements on initially entangled qubits.
Jieshan Huang+9 more
doaj +1 more source
Eigenvalue Approach to Dense Clusters in Hypergraphs
ABSTRACT In this article, we investigate the problem of finding in a given weighted hypergraph a subhypergraph with the maximum possible density. Using the notion of a support matrix we prove that the density of an optimal subhypergraph is equal to ∥ A T A ∥ for an optimal support matrix A. Alternatively, the maximum density of a subhypergraph is equal
Yuly Billig
wiley +1 more source