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Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showing that they are extremely vulnerable to adversarial attacks…

Machine Learning · Computer Science 2019-12-20 Aleksandar Bojchevski , Stephan Günnemann

We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important application of such tests is the detection of differentially…

Quantitative Methods · Quantitative Biology 2014-05-16 Laurent Jacob , Pierre Neuvial , Sandrine Dudoit

Consider property testing on bounded degree graphs and let $\varepsilon>0$ denote the proximity parameter. A remarkable theorem of Newman-Sohler (SICOMP 2013) asserts that all properties of planar graphs (more generally hyperfinite) are…

Data Structures and Algorithms · Computer Science 2024-05-10 Sabyasachi Basu , Akash Kumar , C. Seshadhri

A hypergraph is said to be $1$-Sperner if for every two hyperedges the smallest of their two set differences is of size one. We present several applications of $1$-Sperner hypergraphs and their structure to graphs. In particular, we…

Combinatorics · Mathematics 2018-05-30 Endre Boros , Vladimir Gurvich , Martin Milanič

Directed graphs have long been used to gain understanding of the structure of semigroups, and recently the structure of directed graph semigroups has been investigated resulting in a characterization theorem and an analog of Fruct's…

Combinatorics · Mathematics 2015-06-09 Tien Chih , Demitri Plessas

Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the…

Machine Learning · Computer Science 2022-04-06 Liang Chen , Jintang Li , Jiaying Peng , Tao Xie , Zengxu Cao , Kun Xu , Xiangnan He , Zibin Zheng , Bingzhe Wu

We study directed random graphs (random graphs whose edges are directed), and present new results on the so-called strong components of those graphs. We provide analytic and simulation results on two special classes of strong component,…

Condensed Matter · Physics 2007-05-23 Valmir C. Barbosa , Raul Donangelo , Sergio R. Souza

Small language models fine-tuned for graph property estimation have demonstrated strong in-distribution performance, yet their generalization capabilities beyond training conditions remain poorly understood. In this work, we systematically…

Machine Learning · Computer Science 2026-04-21 Michal Podstawski

In this paper, we introduce a class of graphs which we call average hereditary graphs. Many graphs that occur in the usual graph theory applications belong to this class of graphs. Many popular types of graphs fall under this class, such as…

Discrete Mathematics · Computer Science 2025-08-11 Syed Mujtaba Hassan , Shahid Hussain

Answering an open question from 2007, we construct infinite $k$-crossing-critical families of graphs that contain vertices of any prescribed odd degree, for any sufficiently large~$k$. To answer this question, we introduce several…

Combinatorics · Mathematics 2019-03-19 Drago Bokal , Mojca Bračič , Marek Derňár , Petr Hliněný

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

A class $\mathcal{G}$ of graphs is hereditary if it is closed under taking induced subgraphs. We investigate the edge-add class, $\mathcal{G}^{\mathrm{add}}$, consisting of graphs that can be made members of $\mathcal{G}$ by adding at most…

Combinatorics · Mathematics 2026-04-10 Jagdeep Singh , Vaidy Sivaraman

Let ${\mathcal D}_{n,d}$ be the set of all $d$-regular directed graphs on $n$ vertices. Let $G$ be a graph chosen uniformly at random from ${\mathcal D}_{n,d}$ and $M$ be its adjacency matrix. We show that $M$ is invertible with probability…

Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, however, can be unreliable, and algorithms are sensitive to…

Machine Learning · Computer Science 2026-04-21 Philipp M. Faller , Dominik Janzing

Graph generators learn a model from a source graph in order to generate a new graph that has many of the same properties. The learned models each have implicit and explicit biases built in, and its important to understand the assumptions…

Social and Information Networks · Computer Science 2016-06-15 Salvador Aguinaga , Tim Weninger

Signed graphs are graphs whose edges get a sign $+1$ or $-1$ (the signature). Signed graphs can be studied by means of graph matrices extended to signed graphs in a natural way. Recently, the spectra of signed graphs have attracted much…

Combinatorics · Mathematics 2019-07-11 Francesco Belardo , Sebastian M. Cioabă , Jack H. Koolen , Jianfeng Wang

This paper studies algorithmic meta theorems for property testing with \emph{constant running time} in the bounded degree model. In (Adler, Harwath 2018) it was shown that on graph classes $\mathcal C^{w}_d$ consisting of all graphs with…

Logic in Computer Science · Computer Science 2026-05-12 Isolde Adler , Jenny Stimpson

We characterize the set of properties of Boolean-valued functions on a finite domain $\mathcal{X}$ that are testable with a constant number of samples. Specifically, we show that a property $\mathcal{P}$ is testable with a constant number…

Data Structures and Algorithms · Computer Science 2016-12-20 Eric Blais , Yuichi Yoshida

We report about the results of the application of modern computer algebra tools for construction of directed strongly regular graphs. The suggested techniques are based on the investigation of non-commutative association schemes and Cayley…

Combinatorics · Mathematics 2015-03-30 Štefan Gyürki , Mikhail Klin

Recently, one has seen a surge of interest in developing such methods including ones for learning such representations for (undirected) graphs (while preserving important properties). However, most of the work to date on embedding graphs…

Social and Information Networks · Computer Science 2018-11-30 Jiankai Sun , Srinivasan Parthasarathy