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.
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Latest papers with no code
Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems
This paper addresses the issue of pilot contamination and scalability in massive MIMO systems.
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks
Although DGNN has recently received considerable attention by AI community and various DGNN models have been proposed, building a distributed system for efficient DGNN training is still challenging.
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
In this paper, we study the effectiveness of graph partitioning for distributed GNN training.
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design
However, designing GNN accelerators faces two fundamental challenges: the high bandwidth requirement of GNN models and the diversity of GNN models.
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm
The graph partitioning problem (GPP) is among the most challenging models in optimization.
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks.
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs
We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs.
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints
It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results.
Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues
We consider a new semidefinite programming relaxation for directed edge expansion, which is obtained by adding triangle inequalities to the reweighted eigenvalue formulation.
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering
The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets.
