Package: python3-torch-cluster (1.6.3-2)

[ Source: pytorch-cluster  ]

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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• dep: libc6 (>= 2.32)

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• dep: libgcc-s1 (>= 3.0)

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• dep: libstdc++6 (>= 14)

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• dep: libtorch2.6 (>= 2.6.0+dfsg)

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• dep: python3

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  dep: python3 (<< 3.14)

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