On the complexity of distributed graph coloring

Kühn, Fabian; Wattenhofer, Rogert

doi:10.1145/1146381.1146387

Cited by 167 publications

(184 citation statements)

References 19 publications

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“…There is an expanding experimental literature on collective problem solving in networks, primarily using variants of the "distributed graph coloring problem" as an experimental task (e.g., Kuhn and Wattenhofer, 2006;Kearns, Suri and Montfort, 2006;Kearns, et. Al, 5 2009;Judd, Kearns, and Vorobeychick, 2010).…”

Section: Searching Information Spaces: Clustering Aids Performance?mentioning

confidence: 99%

Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces

Shore

Bernstein²,

Lazer

2014

SSRN Journal

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Using data from a novel laboratory experiment on complex problem solving in which we varied the network structure of 16-person organizations, we investigate how an organization's network structure shapes performance in problem-solving tasks. Problem solving, we argue, involves both search for information and search for solutions. Our results show that the effect of network clustering is opposite for these two important and complementary forms of search. Dense clustering encourages members of a network to generate more diverse information, but discourages them from generating diverse theories: in the language of March (1991), clustering promotes exploration in information space, but decreases exploration in solution space. Previous research, generally focusing on only one of those two spaces at a time, has produced an inconsistent understanding of the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we were able to bring disparate results under a single theoretical roof and clarify the effects of network clustering on problem-solving behavior and performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals the challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one antecedent of successful problem solving may harm the other.

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Section: Searching Information Spaces: Clustering Aids Performance?mentioning

confidence: 99%

Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces

Shore

Bernstein²,

Lazer

2014

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…This computation is performed in parallel using the KW algorithm. 36,37) We applied this method to a two-dimensional magnetic dipolar system on an L×L square lattice to examine its parallelization efficiency. The result showed that, in the case of L = 2304, the speed of computation increased about 102 times by parallel computation with 288 processors.…”

Section: Discussionmentioning

confidence: 99%

“…2.3.2, we explain a basic color reduction al- gorithm for the distributed graph coloring. This algorithm is used in an algorithm proposed by Kuhn and Wattenhofer, 36,37) which is used in this study. This algorithm is explained in Sect.…”

Section: Parallel Computation Of the Vertex Coloringmentioning

confidence: 99%

“…36) We hereafter call it the KW algorithm. The KW algorithm markedly reduces the running time t R by applying the BCR algorithm recursively.…”

Section: Kw Algorithmmentioning

confidence: 99%

“…This computation is performed in a parallel fashion using an algorithm proposed by Kuhn and Wattenhofer. 36,37) The paper is organized as follows: In Sect. 2, we briefly explain the SCO method and describe the parallel computation of the vertex coloring, which is a key feature of the present method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems

2015

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We present a method of parallelizing the stochastic cutoff (SCO) method, which is a Monte-Carlo method for longrange interacting systems. After interactions are eliminated by the SCO method, we subdivide a lattice into noninteracting interpenetrating sublattices. This subdivision enables us to parallelize the Monte-Carlo calculation in the SCO method. Such subdivision is found by numerically solving the vertex coloring of a graph created by the SCO method. We use an algorithm proposed by Kuhn and Wattenhofer to solve the vertex coloring by parallel computation. This method was applied to a two-dimensional magnetic dipolar system on an L × L square lattice to examine its parallelization efficiency. The result showed that, in the case of L = 2304, the speed of computation increased about 102 times by parallel computation with 288 processors.

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On the complexity of distributed graph coloring with local minimality constraints

Gavoille¹,

Klasing²,

Kosowski³

et al. 2009

Networks

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Distributed greedy coloring is an interesting and intuitive variation of the standard coloring problem. Given an order among the colors, a coloring is said to be greedy if there does not exist a vertex for which its associated color can be replaced by a color of lower position in the fixed order without violating the property that neighboring vertices must receive different colors. We consider the problems of Greedy Coloring and Largest First Coloring (a variant of greedy coloring with strengthened constraints) in the Linial model of distributed computation, providing lower and upper bounds and a comparison to the ( + 1)-Coloring and Maximal Independent Set problems, with being the maximum vertex degree in G.

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On the complexity of distributed graph coloring

Cited by 167 publications

References 19 publications

Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces

Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces

Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems

On the complexity of distributed graph coloring with local minimality constraints

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