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A graph $G$ is said to be a `set graph' if it admits an acyclic orientation that is also `extensional', in the sense that the out-neighborhoods of its vertices are pairwise distinct. Equivalently, a set graph is the underlying graph of the…

Discrete Mathematics · Computer Science 2015-03-20 Martin Milanič , Romeo Rizzi , Alexandru I. Tomescu

The expansion of a hypergraph, a natural extension of the notion of expansion in graphs, is defined as the minimum over all cuts in the hypergraph of the ratio of the number of the hyperedges cut to the size of the smaller side of the cut.…

Data Structures and Algorithms · Computer Science 2014-04-18 Anand Louis , Yury Makarychev

Expander graphs, due to their mixing properties, are useful in many algorithms and combinatorial constructions. One can produce an expander graph with high probability by taking a random graph (e.g., the union of $d$ random bijections for a…

Combinatorics · Mathematics 2024-05-30 Geoffroy Caillat-Grenier

We present a simple greedy procedure to compute an $(\alpha,\beta)$-spanner for a graph $G$. We then show that this procedure is useful for building fault-tolerant spanners, as well as spanners for weighted graphs. Our first main result is…

Data Structures and Algorithms · Computer Science 2026-03-19 Elizaveta Popova , Elad Tzalik

Computing maximum independent sets in graphs is an important problem in computer science. In this paper, we develop an evolutionary algorithm to tackle the problem. The core innovations of the algorithm are very natural combine operations…

Data Structures and Algorithms · Computer Science 2015-02-06 Sebastian Lamm , Peter Sanders , Christian Schulz

In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabular features (e.g., original node or edge features) in a…

Machine Learning · Computer Science 2025-08-07 Ashkan Golgoon , Ryan Franks , Khashayar Filom , Arjun Ravi Kannan

There has been an explosion of interest in statistical models for analyzing network data, and considerable interest in the class of exponential random graph (ERG) models, especially in connection with difficulties in computing maximum…

Machine Learning · Statistics 2009-01-05 Stephen E. Fienberg , Alessandro Rinaldo , Yi Zhou

In spite of the plethora of success stories with graph neural networks (GNNs) on modelling graph-structured data, they are notoriously vulnerable to over-squashing, whereby tasks necessitate the mixing of information between distance pairs…

Machine Learning · Computer Science 2025-05-20 JJ Wilson , Maya Bechler-Speicher , Petar Veličković

Jord\'an and Tanigawa recently introduced the $d$-dimensional algebraic connectivity $a_d(G)$ of a graph $G$. This is a quantitative measure of the $d$-dimensional rigidity of $G$ which generalizes the well-studied notion of spectral…

Combinatorics · Mathematics 2023-04-05 Alan Lew , Eran Nevo , Yuval Peled , Orit E. Raz

We present time-efficient distributed algorithms for decomposing graphs with large edge or vertex connectivity into multiple spanning or dominating trees, respectively. As their primary applications, these decompositions allow us to achieve…

Data Structures and Algorithms · Computer Science 2013-11-22 Keren Censor-Hillel , Mohsen Ghaffari , Fabian Kuhn

The efficient parallel execution of complex computations requires balancing the workload across processors while minimizing the communication between them. This inherent trade-off is often captured by graph partitioning or DAG scheduling…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-05-04 Pál András Papp , Toni Böhnlein , A. N. Yzelman

A set of vertices of a graph is distinguishing if the only automorphism that preserves it is the identity. The minimal size of such sets, if they exist, is the distinguishing cost. The distinguishing costs of vertex transitive cubic graphs…

Combinatorics · Mathematics 2025-07-09 Ted Dobson , Ademir Hujdurović , Wilfried Imrich , Ronald Ortner

Double triangle expansion is an operation on $4$-regular graphs with at least one triangle which replaces a triangle with two triangles in a particular way. We study the class of graphs which can be obtained by repeated double triangle…

Combinatorics · Mathematics 2019-04-16 Mohamed Laradji , Marni Mishna , Karen Yeats

In this work, we introduce a novel evaluation framework for generative models of graphs, emphasizing the importance of model-generated graph overlap (Chanpuriya et al., 2021) to ensure both accuracy and edge-diversity. We delineate a…

Machine Learning · Computer Science 2023-12-07 Sudhanshu Chanpuriya , Cameron Musco , Konstantinos Sotiropoulos , Charalampos Tsourakakis

Graph exploration is one of the fundamental tasks performed by a mobile agent in a graph. An $n$-node graph has unlabeled nodes, and all ports at any node of degree $d$ are arbitrarily numbered $0,\dots, d-1$. A mobile agent, initially…

Data Structures and Algorithms · Computer Science 2023-03-21 Andrzej Pelc

Making cut generating functions (CGFs) computationally viable is a central question in modern integer programming research. One would like to find CGFs that are simultaneously good, i.e., there are good guarantees for the cutting planes…

Optimization and Control · Mathematics 2019-02-04 Amitabh Basu , Sriram Sankaranarayanan

We construct a family of maximal linklessly embeddable graphs on $n$ vertices and $3n-5$ edges for all $n\ge 10$, and another family on $n$ vertices and $m< \frac{25n}{12}-\frac{1}{4}$ edges for all $n\ge 13$. The latter significantly…

Geometric Topology · Mathematics 2023-09-13 Ramin Naimi , Andrei Pavelescu , Elena Pavelescu

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

Recently much attention has been paid to deep generative models, since they have been used to great success for variational inference, generation of complex data types, and more. In most all of these settings, the goal has been to find a…

Machine Learning · Statistics 2019-03-19 Sean R. Bittner , John P. Cunningham

Spanner constructions focus on the initial design of the network. However, networks tend to improve over time. In this paper, we focus on the improvement step. Given a graph and a budget $k$, which $k$ edges do we add to the graph to…

Computational Geometry · Computer Science 2024-07-08 Kevin Buchin , Maike Buchin , Joachim Gudmundsson , Sampson Wong
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