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We introduce probabilistic embeddings using Laplacian priors (PELP). The proposed model enables incorporating graph side-information into static word embeddings. We theoretically show that the model unifies several previously proposed…

Computation and Language · Computer Science 2022-04-06 Väinö Yrjänäinen , Måns Magnusson

In the sufficiently sparse case, we find the probability that a uniformly random bipartite graph with given degree sequence contains no edge from a specified set of edges. This enables us to enumerate loop-free digraphs and oriented graphs…

Combinatorics · Mathematics 2026-01-09 Catherine Greenhill , Mahdieh Hasheminezhad , Isaiah Iliffe , Brendan D. McKay

In graph theory, the Szemer\'edi regularity lemma gives a decomposition of the indicator function for any graph $G$ into a structured component, a uniform part, and a small error. This result, in conjunction with a counting lemma that…

Combinatorics · Mathematics 2018-11-22 Sammy Luo

A paradigm that was successfully applied in the study of both pure and algorithmic problems in graph theory can be colloquially summarized as stating that "any graph is close to being the disjoint union of expanders". Our goal in this paper…

Combinatorics · Mathematics 2015-02-03 Guy Moshkovitz , Asaf Shapira

Computing the embedding distribution of a given graph is a fundamental question in topological graph theory. In this article, we extend our viewpoint to a sequence of graphs and consider their asymptotic embedding distributions, which are…

Combinatorics · Mathematics 2025-07-22 Yichao Chen , Wenjie Fang , Zhicheng Gao , Jinlian Zhang

In this paper we introduce a new approach for approximately counting in bounded degree systems with higher-order constraints. Our main result is an algorithm to approximately count the number of solutions to a CNF formula $\Phi$ when the…

Data Structures and Algorithms · Computer Science 2017-03-17 Ankur Moitra

We consider special multiclass spectral, discrepancy, degree, and codegree properties of expanding graph sequences. As we can prove equivalences and implications between them and the definition of the generalized quasirandomness of…

Combinatorics · Mathematics 2018-11-15 Marianna Bolla

We introduce an algorithm which estimates the number of circuits in a graph as a function of their length. This approach provides analytical results for the typical entropy of circuits in sparse random graphs. When applied to real-world…

Statistical Mechanics · Physics 2009-11-11 Enzo Marinari , Remi Monasson , Guilhem Semerjian

We show that the Ramsey number is linear for every uniform hypergraph with bounded-degree. This is a hypergraph extension of the famous theorem for ordinary graphs which Chv\'atal et al. showed in 1983. Our proof is simple, contains the…

Combinatorics · Mathematics 2007-12-14 Yoshiyasu Ishigami

We prove that sparse string graphs in a fixed surface have linear expansion. We extend this result to the more general setting of sparse region intersection graphs over any proper minor-closed class. The proofs are combinatorial and…

Combinatorics · Mathematics 2026-04-10 Nikolai Karol , David R. Wood

Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node embeddings are highly…

Machine Learning · Statistics 2018-11-06 Cătălina Cangea , Petar Veličković , Nikola Jovanović , Thomas Kipf , Pietro Liò

We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many…

Discrete Mathematics · Computer Science 2021-03-22 Dimitris Floros , Nikos Pitsianis , Xiaobai Sun

In this paper we show how to use simple partitioning lemmas in order to embed spanning graphs in a typical member of $G(n,p)$. Let the \emph{maximum density} of a graph $H$ be the maximum average degree of all the subgraphs of $H$. First,…

Combinatorics · Mathematics 2014-09-23 Asaf Ferber , Rajko Nenadov , Ueli Peter

We present the first streaming algorithm for counting an arbitrary hypergraph $H$ of constant size in a massive hypergraph $G$. Our algorithm can handle both edge-insertions and edge-deletions, and is applicable for the distributed setting.…

Data Structures and Algorithms · Computer Science 2013-04-30 He Sun

We consider the number of crossings in a random embedding of a graph, $G$, with vertices in convex position. We give explicit formulas for the mean and variance of the number of crossings as a function of various subgraph counts of $G$.…

Probability · Mathematics 2024-10-14 Santiago Arenas-Velilla , Octavio Arizmendi , J. E. Paguyo

We study the integral and measure theory of the ultraproduct of finite sets. As a main application we construct limit objects for hypergraph sequences. We give a new proof for the Hypergraph Removal Lemma and the Hypergraph Regularity…

Combinatorics · Mathematics 2007-05-23 Gabor Elek , Balazs Szegedy

The sampling of graph signals has recently drawn much attention due to the wide applications of graph signal processing. While a lot of efficient methods and interesting results have been reported to the sampling of band-limited or smooth…

Signal Processing · Electrical Eng. & Systems 2025-01-01 Yingcheng Lai , Li Chai , Jinming Xu

We construct a random $n$-hypergraph for every $n$, in a given pseudofinite field.

Logic · Mathematics 2007-05-23 Ozlem Beyarslan

In this paper, we consider the problem of counting and sampling structures in graphs. We define a class of "edge universal labeling problems"---which include proper $k$-colorings, independent sets, and downsets---and describe simple…

Data Structures and Algorithms · Computer Science 2020-08-20 Christine T. Cheng , Will Rosenbaum

Graph vertex embeddings based on random walks have become increasingly influential in recent years, showing good performance in several tasks as they efficiently transform a graph into a more computationally digestible format while…

Machine Learning · Statistics 2021-07-22 Dominik Kloepfer , Angelica I. Aviles-Rivero , Daniel Heydecker