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We give an extension of a graph result by Alon and Shapira. And it affirmatively settles a question on property testing raised by them. All monotone hypergraph properties and all hereditary partite hypergraph properties are testable. Our…

Combinatorics · Mathematics 2008-03-24 Yoshiyasu Ishigami

Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this gap, we study an aggregate-and-readout model that encompasses…

Machine Learning · Statistics 2026-02-20 Juntong Chen , Claire Donnat , Olga Klopp , Johannes Schmidt-Hieber

We relativise the Thomassen--Woess definition of accessibility in graphs, defining what it means for a graph to be accessible relative to a peripheral system. In the case of locally finite, quasi-transitive graphs, we characterise relative…

Combinatorics · Mathematics 2026-05-14 Joseph Paul MacManus

The theory of graphons comes with the so-called cut norm and the derived cut distance. The cut norm is finer than the weak* topology (when considering the predual of $L^{1}$-functions). Dole\v{z}al and Hladk\'y [J. Combin. Theory Ser. B 137…

Combinatorics · Mathematics 2022-04-19 Martin Doležal , Jan Grebík , Jan Hladký , Israel Rocha , Václav Rozhoň

We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types. We investigate various structural and numerical properties of the graphs in relation to neural network test accuracy. We…

Machine Learning · Computer Science 2020-12-03 Romuald A. Janik , Aleksandra Nowak

Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set. In this paper, we consider a statistical setting for semi-supervised learning and…

Machine Learning · Computer Science 2015-02-17 Aamir Anis , Aly El Gamal , A. Salman Avestimehr , Antonio Ortega

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, as a function of their number of layers, to distinguish…

Machine Learning · Statistics 2020-05-14 Abram Magner , Mayank Baranwal , Alfred O. Hero

We study the behaviour of random labelled and unlabelled cographs with n vertices as n tends to infinity. Our main result is a novel probabilistic limit in the space of graphons.

Probability · Mathematics 2019-06-26 Benedikt Stufler

We examine a hierarchy of equivalence classes of quasi-random properties of Boolean Functions. In particular, we prove an equivalence between a number of properties including balanced influences, spectral discrepancy, local strong…

Combinatorics · Mathematics 2022-09-09 Fan Chung , Nicholas Sieger

Szemeredi's regularity lemma is an important tool in graph theory which has applications throughout combinatorics. In this paper we prove an analogue of Szemeredi's regularity lemma in the context of abelian groups and use it to derive some…

Combinatorics · Mathematics 2007-05-23 Ben Green

The semi-random graph process is a single-player game that begins with an empty graph on $n$ vertices. In each round, a vertex $u$ is presented to the player independently and uniformly at random. The player then adaptively selects a vertex…

Combinatorics · Mathematics 2024-03-05 Natalie C. Behague , Trent G. Marbach , Pawel Pralat , Andrzej Rucinski

In communication field, an important issue is to group users and base stations to as many as possible subnetworks satisfying certain interference constraints. These problems are usually formulated as a graph partition problems which…

Combinatorics · Mathematics 2020-09-30 Chicheng Ma , Yucong Tang , Guanghui Wang , Guiying Yan , Bo Bai

We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is…

Machine Learning · Statistics 2022-02-14 Madeline Navarro , Santiago Segarra

A quasisymmetric graph is a curve whose projection onto a line is a quasisymmetric map. We show that this class of curves is related to solutions of the reduced Beltrami equation and to a generalization of the Zygmund class $\Lambda_*$.…

Complex Variables · Mathematics 2012-11-13 Leonid V. Kovalev , Jani Onninen

In the past two decades, significant advances have been made in understanding the structural and functional properties of biological networks, via graph-theoretic analysis. In general, most graph-theoretic studies are conducted in the…

Physics and Society · Physics 2013-10-21 Michelle Rudolph-Lilith , Lyle E. Muller

We prove several criteria for quasi-isometry between non-locally-finite graphs and their structure trees. Results of M\"oller in \cite{moeller92ends2} for locally finite and transitive graphs are generalized. We also give a criterion which…

Combinatorics · Mathematics 2007-05-23 Bernhard Krön

We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random graph model, which is…

Machine Learning · Statistics 2022-12-21 Madeline Navarro , Santiago Segarra

A result of Simonovits and S\'os states that for any fixed graph $H$ and any $\epsilon > 0$ there exists $\delta > 0$ such that if $G$ is an $n$-vertex graph with the property that every $S \subseteq V(G)$ contains $p^{e(H)} |S|^{v(H)} \pm…

Combinatorics · Mathematics 2016-12-23 David Conlon , Jacob Fox , Benny Sudakov

Subgraph densities have been defined, and served as basic tools, both in the case of graphons (limits of dense graph sequences) and graphings (limits of bounded-degree graph sequences). While limit objects have been described for the…

Combinatorics · Mathematics 2021-05-11 Dávid Kunszenti-Kovács , László Lovász , Balázs Szegedy

Graphons have traditionally served as limit objects for dense graph sequences, with the cut distance serving as the metric for convergence. However, sparse graph sequences converge to the trivial graphon under the conventional definition of…

Signal Processing · Electrical Eng. & Systems 2023-09-12 Xingchao Jian , Feng Ji , Wee Peng Tay
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