Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.8
Journals
Algorithms
Special Issues
Graph Partitioning: Theory, Engineering, and Applications
share announcement

Graph Partitioning: Theory, Engineering, and Applications

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 20423

Special Issue Editors

Dr. Christian Schulz

E-Mail Website
Guest Editor
Faculty of Computer Science, Research Group Theory and Applications of Algorithms, University of Vienna, 1010 Vienna, Austria
Interests: graph partitioning; algorithm engineering; parallel algorithms; big data
Dr. Darren Strash

E-Mail Website
Guest Editor
Department of Computer Science, Hamilton College, Clinton, NY 13323, USA
Interests: theoretical computer science; combinatorial optimization; algorithm engineering; geometric and graph algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of graph partitioning to this Special Issue, Graph Partitioning: Theory, Engineering, and Applications. All facets of graph partitioning will be considered, including shared-memory parallel and distributed algorithms, streaming algorithms, exact algorithms, approximation algorithms, local search, genetic algorithms, metaheuristics, hardness results, parameterized algorithms, variations in objective functions, and alternate partitioning primitives (such as edge partitioning) or process mapping algorithms. High-quality papers are solicited to address both the theoretical and practical issues of graph partitioning. Submissions on graph partitioning’s impact on existing applications, and the introduction of new applications with experiments are highly encouraged.

Dr. Christian Schulz
Dr. Darren Strash
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • graph partitioning
  • edge partitioning
  • exact algorithms
  • approximate algorithms
  • partitioning heuristics and metaheuristics
  • evolutionary algorithms
  • complexity of partitioning algorithms
  • algorithms for multi-objective partitioning
  • partitioning algorithms for distributed processing
  • engineering efficient graph partitioners
  • shared-memory algorithms
  • distributed algorithms

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 842 KiB  
Article
Correspondence between Multilevel Graph Partitions and Tree Decompositions
by Michael Hamann and Ben Strasser
Algorithms 2019, 12(9), 198; https://doi.org/10.3390/a12090198 - 17 Sep 2019
Cited by 1 | Viewed by 4375
Abstract
We present a mapping between rooted tree decompositions and node separator based multilevel graph partitions. Significant research into both tree decompositions and graph partitions exists. We hope that our result allows for an easier knowledge transfer between the two research avenues. Full article
(This article belongs to the Special Issue Graph Partitioning: Theory, Engineering, and Applications)
►▼ Show Figures

Figure 1

20 pages, 355 KiB  
Article
Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
by Lars Gottesbüren, Michael Hamann, Tim Niklas Uhl and Dorothea Wagner
Algorithms 2019, 12(9), 196; https://doi.org/10.3390/a12090196 - 16 Sep 2019
Cited by 15 | Viewed by 4731
Abstract
Graph partitioning has many applications. We consider the acceleration of shortest path queries in road networks using Customizable Contraction Hierarchies (CCH). It is based on computing a nested dissection order by recursively dividing the road network into parts. Recently, with FlowCutter and Inertial [...] Read more.
Graph partitioning has many applications. We consider the acceleration of shortest path queries in road networks using Customizable Contraction Hierarchies (CCH). It is based on computing a nested dissection order by recursively dividing the road network into parts. Recently, with FlowCutter and Inertial Flow, two flow-based graph bipartitioning algorithms have been proposed for road networks. While FlowCutter achieves high-quality results and thus fast query times, it is rather slow. Inertial Flow is particularly fast due to the use of geographical information while still achieving decent query times. We combine the techniques of both algorithms to achieve more than six times faster preprocessing times than FlowCutter and even faster queries on the Europe road network. We show that, using 16 cores of a shared-memory machine, this preprocessing needs four minutes. Full article
(This article belongs to the Special Issue Graph Partitioning: Theory, Engineering, and Applications)
►▼ Show Figures

Figure 1

19 pages, 11617 KiB  
Article
Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
by Hristo N. Djidjev, Georg Hahn, Susan M. Mniszewski, Christian F. A. Negre and Anders M. N. Niklasson
Algorithms 2019, 12(9), 187; https://doi.org/10.3390/a12090187 - 7 Sep 2019
Cited by 8 | Viewed by 3731
Abstract
The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several advanced algorithms relying on evaluations of matrix polynomials have [...] Read more.
The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several advanced algorithms relying on evaluations of matrix polynomials have been published in the literature for such simulations. We aim to use a special type of graph partitioning to efficiently parallelize these computations. For this, we create a graph representing the zero–nonzero structure of a thresholded density matrix, and partition that graph into several components. Each separate submatrix (corresponding to each subgraph) is then substituted into the matrix polynomial, and the result for the full matrix polynomial is reassembled at the end from the individual polynomials. This paper starts by introducing a rigorous definition as well as a mathematical justification of this partitioning problem. We assess the performance of several methods to compute graph partitions with respect to both the quality of the partitioning and their runtime. Full article
(This article belongs to the Special Issue Graph Partitioning: Theory, Engineering, and Applications)
►▼ Show Figures

Figure 1

27 pages, 591 KiB  
Article
Distributed Balanced Partitioning via Linear Embedding †
by Kevin Aydin, MohammadHossein Bateni and Vahab Mirrokni
Algorithms 2019, 12(8), 162; https://doi.org/10.3390/a12080162 - 10 Aug 2019
Cited by 14 | Viewed by 6332
Abstract
Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to [...] Read more.
Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced-partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks such as MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, for example, via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, and minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, for example, a label-propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: For instance, compared to the label-propagation algorithm, we report an improvement of 15–25% in the cut value. We also observe that our algorithms admit scalable distributed implementation for any number of partitions. Finally, we explain three applications of this work at Google: (1) Balanced partitioning is used to route multi-term queries to different replicas in Google Search backend in a way that reduces the cache miss rates by ≈ 0.5 % , which leads to a double-digit gain in throughput of production clusters. (2) Applied to the Google Maps Driving Directions, balanced partitioning minimizes the number of cross-shard queries with the goal of saving in CPU usage. This system achieves load balancing by dividing the world graph into several “shards”. Live experiments demonstrate an ≈ 40 % drop in the number of cross-shard queries when compared to a standard geography-based method. (3) In a job scheduling problem for our data centers, we use balanced partitioning to evenly distribute the work while minimizing the amount of communication across geographically distant servers. In fact, the hierarchical nature of our solution goes well with the layering of data center servers, where certain machines are closer to each other and have faster links to one another. Full article
(This article belongs to the Special Issue Graph Partitioning: Theory, Engineering, and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop