# graph partitioning

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

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.

# 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.

# Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls

Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding.

# 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.

# Distributed Evolutionary Graph Partitioning

We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow Partitioner).

# Think Locally, Act Globally: Perfectly Balanced Graph Partitioning

We present a novel local improvement scheme for the perfectly balanced graph partitioning problem.

# Parallel Graph Partitioning for Complex Networks

This paper addresses this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering.

# 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.