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
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
liyaguang/DCRNN
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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.
