graph partitioning
57 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
HAM
Most implemented papers
Accurate and versatile 3D segmentation of plant tissues at cellular resolution
hci-unihd/plant-seg
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eLife 2020
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs.
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
dmlc/dgl
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To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints.
Characterizing and comparing external measures for the assessment of cluster analysis and community detection
For a collection of candidate measures, it first consists in describing their behavior by computing them for a generated dataset of partitions, obtained by applying a set of predefined parametric partition transformations.
Refining a -nearest neighbor graph for a computationally efficient spectral clustering
mashaan14/Spectral-Clustering
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Pattern Recognition 2021
We proposed a refined version of -nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
Buffered Streaming Graph Partitioning
On the one hand, there are streaming algorithms that have been adopted to partition massive graph data on small machines.
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking
jiaweihe1996/GMTracker
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CVPR 2021
Then the association problem turns into a general graph matching between tracklet graph and detection graph.
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning Problem
We also derive an approximation bound for the algorithm in terms of the maximum number of hypotheses speakers.
Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping
More specifically, we design a Geometry-aware Association GNN that utilizes spatial information of the keypoints and learns local affinity from the global context.
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
alga-hopf/drl-graph-partitioning
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The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU.
RAMA: A Rapid Multicut Algorithm on GPU
pawelswoboda/rama
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CVPR 2022
We propose a highly parallel primal-dual algorithm for the multicut (a. k. a.
