python3-torch-cluster - 1.6.3-2 main

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:
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* 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
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All included operations work on varying data types and are implemented both
for CPU and GPU.
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This package installs the library for Python 3.