# graph partitioning

40 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.

## Most implemented papers

# 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 .

# Graph Neural Network Based Coarse-Grained Mapping Prediction

The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation.

# A Min-max Cult Algorithm for Graph Partitioning and Data Clustering

In this paper, we propose a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle.

# The Product Cut

We introduce a theoretical and algorithmic framework for multi-way graph partitioning that relies on a multiplicative cut-based objective.

# 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.

# 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.