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
Latest papers
Creating Multi-Level Skill Hierarchies in Reinforcement Learning
What is a useful skill hierarchy for an autonomous agent?
Inductive Graph Unlearning
happy2git/guide
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To extend machine unlearning to graph data, \textit{GraphEraser} has been proposed.
Distributed Graph Embedding with Information-Oriented Random Walks
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks.
A parameter-free graph reduction for spectral clustering and SpectralNet
mashaan14/SC-parameter-free
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We introduce a graph reduction method that does not require any parameters.
Random projection tree similarity metric for SpectralNet
mashaan14/RPTree-SpectralNet
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Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to $k$-nn graph with a distance metric.
Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering
mashaan14/Spectral-Clustering
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We proposed a refined version of $k$-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
Graph Construction using Principal Axis Trees for Simple Graph Convolution
We introduce a graph construction scheme that constructs the adjacency matrix $A$ using unsupervised and supervised information.
Approximate spectral clustering density-based similarity for noisy datasets
Also, CONN could be tricked by noisy density between clusters.
Approximate spectral clustering with eigenvector selection and self-tuned $k$
The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption.
Random Projection Forest Initialization for Graph Convolutional Networks
In a $k$-nn graph, points are restricted to have a fixed number of edges, and all edges in the graph have equal weights.
