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# graph partitioning Edit

13 papers with code · Graphs

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# 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.

2,245

# Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters

More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase .

751

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

24 Sep 2019liyaguang/DCRNN

We demonstrate the efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11, 160 sensor locations.

463

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

24 Jan 2018NervanaSystems/ngraph-python

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.

214

# Improving Coarsening Schemes for Hypergraph Partitioning by Exploiting Community Structure

We present an improved coarsening process for multilevel hypergraph partitioning that incorporates global information about the community structure.

82

# Towards Efficient Large-Scale Graph Neural Network Computing

This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for.

71

# 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.

31

# Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts

25 May 2019constantinpape/cluster_tools

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

15

# Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

Further, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs.

9

# 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.

9