Graph partitioning and disturbed diffusion

Meyerhenke, Henning; Monien, Burkhard; Schamberger, Stefan

doi:10.1016/j.parco.2009.09.006

Cited by 44 publications

(46 citation statements)

References 32 publications

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“…Meyerhenke et al [MMS09b] present a similarity measure based on diffusion that is employed within the Bubble framework. This diffusive approach bears some conceptual resemblance to spectral partitioning, but with advantages in quality [MS12].…”

Section: Random Walks and Diffusionmentioning

confidence: 99%

“…The flow value f ij between blocks i and j specifies how many nodes have to be migrated from i to j in order to balance the partition. As a key and novel property for obtaining good solutions, to determine which nodes should be migrated in which order, the diffusive similarity values computed before within the Bubble framework are used [MMS09b,Mey08].…”

Section: Random Walks and Diffusionmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Graph Partitioning

Buluç

Meyerhenke

Safro

et al. 2016

Lecture Notes in Computer Science

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401

300

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Section: Random Walks and Diffusionmentioning

confidence: 99%

Section: Random Walks and Diffusionmentioning

confidence: 99%

Recent Advances in Graph Partitioning

Buluç

Meyerhenke

Safro

et al. 2016

Lecture Notes in Computer Science

Self Cite

401

300

View full text Add to dashboard Cite

show abstract

“…Although the most important metric for edge-cut graph partitioning is the size of the edge-cut (or energy), a number of studies [Hendrickson 1998] show that this metric alone is not enough to measure the partitioning quality. Several metrics are, therefore, defined and used in the literature [Meyerhenke et al 2008[Meyerhenke et al , 2009, among which we selected the following in our evaluations:…”

Section: Edge-cut Partitioningmentioning

confidence: 99%

“…The very large scale of the graphs we target poses a major challenge. Although numerous algorithms are known for graph partitioning [Enright et al 2002;Kumar 1999a, 1998;Kernighan and Lin 1970;Meyerhenke et al 2008Meyerhenke et al , 2009Schulz 2012, 2011], including parallel ones, most of the techniques involved assume a form of cheap random access to the entire graph. In contrast to this, large-scale graphs do not fit into the main memory of a single computer; in fact, they often do not fit on a single local file system either.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Algorithm for Large-Scale Graph Partitioning

Rahimian

Payberah²,

Girdzijauskas

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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Balanced graph partitioning is an NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems, including the optimal storage of large sets of graph-structured data over several hosts. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable because they typically involve frequent global operations over the entire graph. In this article, we propose a fully distributed algorithm called JA-BE-JA that uses local search and simulated annealing techniques for two types of graph partitioning: edge-cut partitioning and vertex-cut partitioning. The algorithm is massively parallel: There is no central coordination, each vertex is processed independently, and only the direct neighbors of a vertex and a small subset of random vertices in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We show that the minimal edge-cut value empirically achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as METIS. In particular, on large social networks, JA-BE-JA outperforms METIS. We also show that JA-BE-JA computes very low vertex-cuts, which are proved significantly more effective than edge-cuts for processing most real-world graphs.

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“…The very large scale of the graphs we target poses a major challenge. Although a very large number of algorithms are known for graph partitioning [3], [4], [5], [6], [7], [8], [9], [10], including parallel ones, most of the techniques involved assume a form of cheap random access to the entire graph. In contrast to this, large scale graphs do not fit into the main memory of a single computer, in fact, they often do not fit on a single local file system either.…”

Section: Introductionmentioning

confidence: 99%

JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning

Rahimian

Payberah

Girdzijauskas

et al. 2013

2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-Balanced graph partitioning is a well known NPcomplete problem with a wide range of applications. These applications include many large-scale distributed problems including the optimal storage of large sets of graph-structured data over several hosts-a key problem in today's Cloud infrastructure. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable, because they typically involve frequent global operations over the entire graph. In this paper, we propose a fully distributed algorithm, called JA-BE-JA, that uses local search and simulated annealing techniques for graph partitioning. The algorithm is massively parallel: there is no central coordination, each node is processed independently, and only the direct neighbors of the node, and a small subset of random nodes in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We perform a thorough experimental analysis, which shows that the minimal edge-cut value achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as METIS. In particular, on large social networks JA-BE-JA outperforms METIS, which makes JA-BE-JA-a bottomup, self-organizing algorithm-a highly competitive practical solution for graph partitioning.

show abstract

Graph partitioning and disturbed diffusion

Cited by 44 publications

References 32 publications

Recent Advances in Graph Partitioning

Recent Advances in Graph Partitioning

A Distributed Algorithm for Large-Scale Graph Partitioning

JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning

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