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Node classification is an important problem in graph data management. It is commonly solved by various label propagation methods that work iteratively starting from a few labeled seed nodes. For graphs with arbitrary compatibilities between…

Machine Learning · Computer Science 2020-03-06 Krishna Kumar P. , Paul Langton , Wolfgang Gatterbauer

A fault-tolerant distance labeling scheme assigns a label to each vertex and edge of an undirected weighted graph $G$ with $n$ vertices so that, for any edge set $F$ of size $|F| \leq f$, one can approximate the distance between $p$ and $q$…

Data Structures and Algorithms · Computer Science 2026-04-03 Bernhard Haeupler , Yaowei Long , Antti Roeyskoe , Thatchaphol Saranurak

Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erd\H{o}s-R\'{e}nyi graphs $G(n,\frac{d}{n})$.…

Machine Learning · Statistics 2020-07-21 Jian Ding , Zongming Ma , Yihong Wu , Jiaming Xu

Given an $n$-vertex $m$-edge graph $G$ with non negative edge-weights, the girth of $G$ is the weight of a shortest cycle in $G$. For any graph $G$ with polynomially bounded integer weights, we present a deterministic algorithm that…

Data Structures and Algorithms · Computer Science 2018-10-25 Guillaume Ducoffe

We investigate the problem of sequentially predicting the binary labels on the nodes of an arbitrary weighted graph. We show that, under a suitable parametrization of the problem, the optimal number of prediction mistakes can be…

Machine Learning · Computer Science 2012-12-27 Nicolo' Cesa-Bianchi , Claudio Gentile , Fabio Vitale , Giovanni Zappella

Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest…

Machine Learning · Statistics 2019-05-02 Vassilis Kalofolias , Nathanaël Perraudin

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

In the study of deterministic distributed algorithms it is commonly assumed that each node has a unique $O(\log n)$-bit identifier. We prove that for a general class of graph problems, local algorithms (constant-time distributed algorithms)…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-12-24 Mika Göös , Juho Hirvonen , Jukka Suomela

In the Densest k-Subgraph problem, given a graph G and a parameter k, one needs to find a subgraph of G induced on k vertices that contains the largest number of edges. There is a significant gap between the best known upper and lower…

Data Structures and Algorithms · Computer Science 2010-01-19 Aditya Bhaskara , Moses Charikar , Eden Chlamtac , Uriel Feige , Aravindan Vijayaraghavan

We study the Minimum Crossing Number problem: given an $n$-vertex graph $G$, the goal is to find a drawing of $G$ in the plane with minimum number of edge crossings. This is one of the central problems in topological graph theory, that has…

Data Structures and Algorithms · Computer Science 2010-12-02 Julia Chuzhoy

In the weighted (minimum) {\sf Label $s$-$t$ Cut} problem, we are given a (directed or undirected) graph $G=(V,E)$, a label set $L = \{\ell_1, \ell_2, \dots, \ell_q \}$ with positive label weights $\{w_\ell\}$, a source $s \in V$ and a sink…

Data Structures and Algorithms · Computer Science 2020-11-13 Peng Zhang

We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph coloring. We train our model using reinforcement learning,…

Machine Learning · Computer Science 2022-05-11 Lukas Gianinazzi , Maximilian Fries , Nikoli Dryden , Tal Ben-Nun , Maciej Besta , Torsten Hoefler

We study the densest subgraph problem and its NP-hard densest at-most-$k$ subgraph variant through the lens of learning-augmented algorithms. We show that, given a reasonably accurate predictor that estimates whether a node belongs to the…

Data Structures and Algorithms · Computer Science 2026-04-16 Thai Bui , Luan Nguyen , Hoa T. Vu

In a temporal graph the edge set dynamically changes over time according to a set of time-labels associated with each edge that indicates at which time-steps the edge is available. Two vertices are connected if there is a path connecting…

Data Structures and Algorithms · Computer Science 2025-04-24 Daniele Carnevale , Gianlorenzo D'Angelo , Martin Olsen

In this paper we look at the problem of adjacency labeling of graphs. Given a family of undirected graphs the problem is to determine an encoding-decoding scheme for each member of the family such that we can decode the adjacency…

Discrete Mathematics · Computer Science 2022-01-27 Avah Banerjee

Graph-based nearest neighbor search methods have seen a surge of popularity in recent years, offering state-of-the-art performance across a wide variety of applications. Central to these methods is the task of constructing a sparse…

Data Structures and Algorithms · Computer Science 2025-10-24 Alex Conway , Laxman Dhulipala , Martin Farach-Colton , Rob Johnson , Ben Landrum , Christopher Musco , Yarin Shechter , Torsten Suel , Richard Wen

In this paper, we show a connection between a certain online low-congestion routing problem and an online prediction of graph labeling. More specifically, we prove that if there exists a routing scheme that guarantees a congestion of…

Data Structures and Algorithms · Computer Science 2008-09-12 Jittat Fakcharoenphol , Boonserm Kijsirikul

The 1-median problem (1MP) on undirected weighted graphs seeks to find a facility location minimizing the total weighted distance to all customer nodes. Although the 1MP can be solved exactly by computing the single-source shortest paths…

Data Structures and Algorithms · Computer Science 2025-09-04 Keisuke Ueta , Wei Wu , Mutsunori Yagiura

A common way of partitioning graphs is through minimum cuts. One drawback of classical minimum cut methods is that they tend to produce small groups, which is why more balanced variants such as normalized and ratio cuts have seen more…

Machine Learning · Computer Science 2024-10-07 Chakib Fettal , Lazhar Labiod , Mohamed Nadif

$\newcommand{\eps}{\varepsilon}$ In this paper, we consider two important problems defined on finite metric spaces, and provide efficient new algorithms and approximation schemes for these problems on inputs given as graph shortest path…

Computational Geometry · Computer Science 2021-02-23 David Eppstein , Sariel Har-Peled , Anastasios Sidiropoulos