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[ Source: pytorch-cluster  ]

Package: python3-torch-cluster (1.6.3-1 and others)

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PyTorch extension library of optimized graph cluster algorithms (Python 3)

This package consists of a small extension library of highly optimized graph cluster algorithms for the use in PyTorch. The package consists of the following clustering algorithms:

 * Graclus from Dhillon et al.: Weighted Graph Cuts without Eigenvectors: A
   Multilevel Approach
 * Voxel Grid Pooling from, e.g., Simonovsky and Komodakis: Dynamic
   Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
 * Iterative Farthest Point Sampling from, e.g. Qi et al.: PointNet++: Deep
   Hierarchical Feature Learning on Point Sets in a Metric Space
 * k-NN and Radius graph generation
 * Clustering based on nearest points
 * Random Walk Sampling from, e.g., Grover and Leskovec: node2vec: Scalable
   Feature Learning for Networks

All included operations work on varying data types and are implemented both for CPU and GPU.

This package installs the library for Python 3.

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Download for all available architectures
Architecture Version Package Size Installed Size Files
amd64 1.6.3-1+b1 264.5 kB1,797.0 kB [list of files]
arm64 1.6.3-1+b1 241.2 kB2,085.0 kB [list of files]
ppc64el 1.6.3-1 262.2 kB2,340.0 kB [list of files]
s390x 1.6.3-1+b1 267.7 kB1,933.0 kB [list of files]