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Massively-parallel graph algorithms have received extensive attention over the past decade, with research focusing on three memory regimes: the superlinear regime, the near-linear regime, and the sublinear regime. The sublinear regime is…

Data Structures and Algorithms · Computer Science 2023-03-01 Orr Fischer , Adi Horowitz , Rotem Oshman

This proposal presents a graph computing framework intending to support both online and offline computing on large dynamic graphs efficiently. The framework proposes a new data model to support rich evolving vertex and edge data types. It…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-12-08 Zhao Yu Dong

When processing a batch of graphs in machine learning models such as Graph Neural Networks (GNN), it is common to combine several small graphs into one overall graph to accelerate processing and remove or reduce the overhead of padding.…

Machine Learning · Computer Science 2022-09-20 Mario Michael Krell , Manuel Lopez , Sreenidhi Anand , Hatem Helal , Andrew William Fitzgibbon

Planning for multi-robot teams in complex environments is a challenging problem, especially when these teams must coordinate to accomplish a common objective. In general, optimal solutions to these planning problems are computationally…

Robotics · Computer Science 2024-03-07 Cora A. Dimmig , Kevin C. Wolfe , Joseph Moore

Finding the connected components of a graph is a fundamental problem with uses throughout computer science and engineering. The task of computing connected components becomes more difficult when graphs are very large, or when they are…

Data Structures and Algorithms · Computer Science 2022-03-29 David Tench , Evan West , Victor Zhang , Michael A. Bender , Abiyaz Chowdhury , J. Ahmed Dellas , Martin Farach-Colton , Tyler Seip , Kenny Zhang

We study stochastic graph optimization problems in a novel distributed setting. As in the standard centralized setting, a random subgraph $G^*$ of a known base graph $G$ is realized by including each edge $e$ independently with a known…

Data Structures and Algorithms · Computer Science 2026-05-21 Keren Censor-Hillel , Aditi Dudeja , George Giakkoupis

In this paper, we study batch parallel algorithms for the dynamic connectivity problem, a fundamental problem that has received considerable attention in the sequential setting. The most well known sequential algorithm for dynamic…

Data Structures and Algorithms · Computer Science 2020-05-19 Umut A. Acar , Daniel Anderson , Guy E. Blelloch , Laxman Dhulipala

In fully dynamic graphs, we know how to maintain a 2-approximation of maximum matching extremely fast, that is, in polylogarithmic update time or better. In a sharp contrast and despite extensive studies, all known algorithms that maintain…

Data Structures and Algorithms · Computer Science 2019-11-06 Soheil Behnezhad , Jakub Łącki , Vahab Mirrokni

Problems from graph drawing, spectral clustering, network flow and graph partitioning can all be expressed in terms of graph Laplacian matrices. There are a variety of practical approaches to solving these problems in serial. However, as…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-07-12 Tristan Konolige , Jed Brown

This paper considers fully dynamic graph algorithms with both faster worst case update time and sublinear space. The fully dynamic graph connectivity problem is the following: given a graph on a fixed set of n nodes, process an online…

Data Structures and Algorithms · Computer Science 2015-09-23 David Gibb , Bruce Kapron , Valerie King , Nolan Thorn

This paper presents a novel meta algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nutshell, PM divides the graph into small subgraphs using our…

Data Structures and Algorithms · Computer Science 2013-09-25 Vincent Blondel , Kyomin Jung , Pushmeet Kohli , Devavrat Shah

Constructing a spanning tree of a graph is one of the most basic tasks in graph theory. We consider a relaxed version of this problem in the setting of local algorithms. The relaxation is that the constructed subgraph is a sparse spanning…

Data Structures and Algorithms · Computer Science 2021-04-28 Reut Levi , Dana Ron , Ronitt Rubinfeld

The classic technique of Baker [J. ACM '94] is the most fundamental approach for designing approximation schemes on planar, or more generally topologically-constrained graphs, and it has been applied in a myriad of different variants and…

Data Structures and Algorithms · Computer Science 2023-11-01 Tuukka Korhonen , Wojciech Nadara , Michał Pilipczuk , Marek Sokołowski

We present a parallel version of the cut-pursuit algorithm for minimizing functionals involving the graph total variation. We show that the decomposition of the iterate into constant connected components, which is at the center of this…

Data Structures and Algorithms · Computer Science 2019-05-08 Hugo Raguet , Loic Landrieu

Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been widely studied for structured sparse models where the sparsity…

Machine Learning · Computer Science 2019-05-10 Baojian Zhou , Feng Chen , Yiming Ying

Increasing interest in integrating advanced robotics within manufacturing has spurred a renewed concentration in developing real-time scheduling solutions to coordinate human-robot collaboration in this environment. Traditionally, the…

Robotics · Computer Science 2020-06-30 Zheyuan Wang , Matthew Gombolay

We show a fully dynamic algorithm for maintaining $(1+\epsilon)$-approximate \emph{size} of maximum matching of the graph with $n$ vertices and $m$ edges using $m^{0.5-\Omega_{\epsilon}(1)}$ update time. This is the first polynomial…

Data Structures and Algorithms · Computer Science 2024-04-30 Sayan Bhattacharya , Peter Kiss , Thatchaphol Saranurak

We consider the problem of maintaining an approximate maximum integral matching in a dynamic graph $G$, while the adversary makes changes to the edges of the graph. The goal is to maintain a $(1+\epsilon)$-approximate maximum matching for…

Data Structures and Algorithms · Computer Science 2022-07-07 Sepehr Assadi , Aaron Bernstein , Aditi Dudeja

Finding a maximum clique in a given graph is one of the fundamental NP-hard problems. We compare two multi-core thread-parallel adaptations of a state-of-the-art branch and bound algorithm for the maximum clique problem, and provide a novel…

Distributed, Parallel, and Cluster Computing · Computer Science 2014-09-05 Ciaran McCreesh , Patrick Prosser

Large-scale graph processing has drawn great attention in recent years. Most of the modern-day datacenter workloads can be represented in the form of Graph Processing such as MapReduce etc. Consequently, a lot of designs for Domain-Specific…

Hardware Architecture · Computer Science 2022-09-07 Khushal Sethi