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The degree splitting problem requires coloring the edges of a graph red or blue such that each node has almost the same number of edges in each color, up to a small additive discrepancy. The directed variant of the problem requires…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-12-24 Mohsen Ghaffari , Juho Hirvonen , Fabian Kuhn , Yannic Maus , Jukka Suomela , Jara Uitto

LCLs or locally checkable labelling problems (e.g. maximal independent set, maximal matching, and vertex colouring) in the LOCAL model of computation are very well-understood in cycles (toroidal 1-dimensional grids): every problem has a…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-05-25 Sebastian Brandt , Juho Hirvonen , Janne H. Korhonen , Tuomo Lempiäinen , Patric R. J. Östergård , Christopher Purcell , Joel Rybicki , Jukka Suomela , Przemysław Uznański

This work concerns the analysis and design of distributed first-order optimization algorithms over time-varying graphs. The goal of such algorithms is to optimize a global function that is the average of local functions using only local…

Optimization and Control · Mathematics 2020-02-17 Akhil Sundararajan , Bryan Van Scoy , Laurent Lessard

In this paper, we investigate how local rules enforced at every node can influence the topology of a network. More precisely, we establish several results on the diameter of trees as a function of the number of nodes, as listed below. These…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-09-04 Nicolas Bousquet , Laurent Feuilloley , Théo Pierron

The complexity of distributed edge coloring depends heavily on the palette size as a function of the maximum degree $\Delta$. In this paper we explore the complexity of edge coloring in the LOCAL model in different palette size regimes. 1.…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-04-20 Yi-Jun Chang , Qizheng He , Wenzheng Li , Seth Pettie , Jara Uitto

We introduce the \emph{local information cost} (LIC), which quantifies the amount of information that nodes in a network need to learn when solving a graph problem. We show that the local information cost presents a natural lower bound on…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-01-23 Peter Robinson

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling…

Information Retrieval · Computer Science 2022-05-10 Junliang Yu , Hongzhi Yin , Xin Xia , Tong Chen , Lizhen Cui , Quoc Viet Hung Nguyen

Distributed peer-to-peer systems are widely popular due to their decentralized nature, which ensures that no peer is critical for the functionality of the system. However, fully decentralized solutions are usually much harder to design, and…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-04-04 Vijeth Aradhya , Christian Scheideler

Can we efficiently compute optimal solutions to instances of a hard problem from optimal solutions to neighboring (i.e., locally modified) instances? For example, can we efficiently compute an optimal coloring for a graph from optimal…

Computational Complexity · Computer Science 2019-06-26 Elisabet Burjons , Fabian Frei , Edith Hemaspaandra , Dennis Komm , David Wehner

The study of Locally Checkable Labelings (LCLs) has led to a remarkably precise characterization of the distributed time complexities that can occur on bounded-degree trees. A central feature of this complexity landscape is the existence of…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-02-17 Gustav Schmid

Graph Balancing is the problem of orienting the edges of a weighted multigraph so as to minimize the maximum weighted in-degree. Since the introduction of the problem the best algorithm known achieves an approximation ratio of $1.75$ and it…

Data Structures and Algorithms · Computer Science 2018-11-05 Klaus Jansen , Lars Rohwedder

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its quadratic computational complexity might lead to inconsistency…

Machine Learning · Computer Science 2022-12-12 Hengrui Zhang , Qitian Wu , Yu Wang , Shaofeng Zhang , Junchi Yan , Philip S. Yu

Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependency between nodes in a graph, and the privacy concern due to…

Machine Learning · Computer Science 2022-03-15 Morteza Ramezani , Weilin Cong , Mehrdad Mahdavi , Mahmut T. Kandemir , Anand Sivasubramaniam

We show an $\Omega\big(\Delta^{\frac{1}{3}-\frac{\eta}{3}}\big)$ lower bound on the runtime of any deterministic distributed $\mathcal{O}\big(\Delta^{1+\eta}\big)$-graph coloring algorithm in a weak variant of the \LOCAL\ model. In…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-09-15 Dan Hefetz , Fabian Kuhn , Yannic Maus , Angelika Steger

In this paper, we study the notion of mending, i.e. given a partial solution to a graph problem, we investigate how much effort is needed to turn it into a proper solution. For example, if we have a partial coloring of a graph, how hard is…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-09-13 Darya Melnyk , Jukka Suomela , Neven Villani

A rich line of work has been addressing the computational complexity of locally checkable labelings (LCLs), illustrating the landscape of possible complexities. In this paper, we study the landscape of LCL complexities under bandwidth…

Data Structures and Algorithms · Computer Science 2021-05-18 Alkida Balliu , Keren Censor-Hillel , Yannic Maus , Dennis Olivetti , Jukka Suomela

In classic distributed graph problems, each instance on a graph specifies a space of feasible solutions (e.g. all proper ($\Delta+1$)-list-colorings of the graph), and the task of distributed algorithm is to construct a feasible solution…

Data Structures and Algorithms · Computer Science 2018-02-20 Weiming Feng , Yitong Yin

We present a deterministic distributed algorithm that computes a $(2\Delta-1)$-edge-coloring, or even list-edge-coloring, in any $n$-node graph with maximum degree $\Delta$, in $O(\log^7 \Delta \log n)$ rounds. This answers one of the…

Data Structures and Algorithms · Computer Science 2017-04-11 Manuela Fischer , Mohsen Ghaffari , Fabian Kuhn

Brooks' theorem states that all connected graphs but odd cycles and cliques can be colored with $\Delta$ colors, where $\Delta$ is the maximum degree of the graph. Such colorings have been shown to admit non-trivial distributed algorithms…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-03-07 Yann Bourreau , Sebastian Brandt , Alexandre Nolin

The last five years of research on distributed graph algorithms have seen huge leaps of progress, both regarding algorithmic improvements and impossibility results: new strong lower bounds have emerged for many central problems and…

Data Structures and Algorithms · Computer Science 2025-01-08 Sebastian Brandt , Yannic Maus , Ananth Narayanan , Florian Schager , Jara Uitto