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graph partitioning Edit

12 papers with code · Graphs

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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.

1,836

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.

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Memetic Multilevel Hypergraph Partitioning

Hypergraph partitioning has a wide range of applications such as VLSI design or scientific computing.

59

Network Flow-Based Refinement for Multilevel Hypergraph Partitioning

We present a refinement framework for multilevel hypergraph partitioning that uses max-flow computations on pairs of blocks to improve the solution quality of a k-way partition.

59

Engineering a direct k-way Hypergraph Partitioning Algorithm

We also remove several further bottlenecks in processing large hyperedges, develop a faster contraction algorithm, and a new adaptive stopping rule for local search.

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k-way Hypergraph Partitioning via n-Level Recursive Bisection

We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time.

59

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.

59

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 .

SOTA for Community Detection on Amazon (NMI metric )

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