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 implementationsMost implemented papers
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
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
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
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
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
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
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
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
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
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
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.