graph partitioning

56 papers with code • 1 benchmarks • 2 datasets

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

Libraries

Use these libraries to find graph partitioning models and implementations
3 papers
357

Most implemented papers

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

NervanaSystems/ngraph-python 24 Jan 2018

The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.

Learning Space Partitions for Nearest Neighbor Search

twistedcubic/learn-to-hash ICLR 2020

Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms.

GAP: Generalizable Approximate Graph Partitioning Framework

saurabhdash/GCN_Partitioning 2 Mar 2019

Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions.

PyTorch-BigGraph: A Large-scale Graph Embedding System

facebookresearch/PyTorch-BigGraph 28 Mar 2019

Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

HongtengXu/s-gwl NeurIPS 2019

Using this concept, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs; the barycenter graph plays the role of the disconnected graph, and since it is learned, so is the clustering.

Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts

constantinpape/cluster_tools 25 May 2019

The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure.

A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time

bit-ml/sfseg 5 Jul 2019

Our method is based on the power iteration for finding the principal eigenvector of a matrix, which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume.

Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning

xiangni/DREAM 19 Nov 2019

In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data.

Spectral Modification of Graphs for Improved Spectral Clustering

ikoutis/spectral-modification NeurIPS 2019

Applying then spectral clustering on $H$ has the potential to produce improved cuts that also exist in $G$ due to the cut similarity.

Generalized Spectral Clustering via Gromov-Wasserstein Learning

trneedham/Spectral-Gromov-Wasserstein 7 Jun 2020

A key insight of the GWL framework toward graph partitioning was to compute GW correspondences from a source graph to a template graph with isolated, self-connected nodes.