graphs-1

graph partitioning

3260 papers • 126 benchmarks • 313 datasets

Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.

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KaHIP/KaHIP

3 papers 360

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HAM

A collection of LFR benchmark graphs

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Most implemented papers

Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters

Alessandro Epasto, Silvio Lattanzi, R. Leme•Thu Aug 03 2017

Ego-splitting is a highly scalable and flexible framework that reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (partitioning) problem.

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Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting

Tanwi Mallick, Prasanna Balaprakash, E. Rask, Jane Macfarlane•Mon Sep 23 2019

This approach uses a graph-partitioning method to decompose a large highway network into smaller networks and trains them independently and demonstrates that the DCRNN model can be used to forecast the speed and flow simultaneously and that the forecasted values preserve fundamental traffic flow dynamics.

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Graph neural network based coarse-grained mapping prediction

G. Wellawatte, Zhiheng Li, M. Chakraborty, Heta A. Gandhi, Chenliang Xu, A. White•Tue Jun 23 2020

This work presents a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem and finds that predicted CG mapping operators indeed result in good CG MD models when used in simulation.

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Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls

Eli Chien, O. Milenkovic, Saurav Prakash, Jin Sima, Chao Pan•Sun Aug 13 2023

This work proposes the first known approach to federated classification in hyperbolic spaces, and tests the method on a collection of diverse data sets, including hierarchical single-cell RNA-seq data from different patients distributed across different repositories that have stringent privacy constraints.

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Distributed Evolutionary Graph Partitioning

P. Sanders, Christian Schulz•Sun Oct 02 2011

A novel distributed evolutionary algorithm, KaFFPaE, is presented, to solve the Graph Partitioning Problem, which makes use of KaFF Pa (Karlsruhe Fast Flow Partitioner), which provides new effective crossover and mutation operators.

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Think Locally, Act Globally: Perfectly Balanced Graph Partitioning

P. Sanders, Christian Schulz•Sun Sep 30 2012

A novel local improvement scheme for the perfectly balanced graph partitioning problem is presented that is fast on the one hand and able to improve or reproduce most of the best known perfectly balanced partitioning results ever reported in the literature.

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Parallel Graph Partitioning for Complex Networks

P. Sanders, Christian Schulz, Henning Meyerhenke•Thu Apr 17 2014

This paper parallelizing and adapting the label propagation technique originally developed for graph clustering, and introducing size constraints, becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning.

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The Product Cut

Arthur Szlam, X. Bresson, T. Laurent, J. V. Brecht•Thu Dec 31 2015

A theoretical and algorithmic framework for multi-way graph partitioning that relies on a multiplicative cut-based objective and an effective algorithm for its optimization that achieves state-of-the-art performance on benchmark data sets is introduced.

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Improving Coarsening Schemes for Hypergraph Partitioning by Exploiting Community Structure

Tobias Heuer, Sebastian Schlag•Sat Dec 31 2016

An improved coarsening process for multilevel hypergraph partitioning that incorporates global information about the community structure is presented that significantly improves the solutions found by the initial partitioning algorithm and consistently improves overall solution quality.

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