| Name | Version | Summary | date |
| molgraph |
0.6.5 |
Graph Neural Networks for Molecular Machine Learning |
2024-02-09 14:18:08 |
| hivegraph |
0.0.2 |
Python Framework based on PyG for working with Graph Neural Networks |
2023-12-29 03:03:57 |
| torch-cluster |
1.6.3 |
PyTorch Extension Library of Optimized Graph Cluster Algorithms |
2023-10-12 06:52:43 |
| ccsd |
0.3.3 |
CCSD (Combinatorial Complex Score-based Diffusion) is a sophisticated score-based diffusion model designed to generate Combinatorial Complexes using Stochastic Differential Equations. This cutting-edge approach enables the generation of complex objects with higher-order structures and relations, thereby enhancing our ability to learn underlying distributions and produce more realistic objects. |
2023-09-18 09:25:22 |
| torch-spatiotemporal |
0.9.4 |
A PyTorch library for spatiotemporal data processing |
2023-08-18 12:14:11 |
| GSLB |
0.1.0 |
GSLB: Graph Structure Learning Benchmark |
2023-07-12 13:10:27 |
| pyg-library |
0.2.0 |
Low-Level Graph Neural Network Operators for PyG |
2023-04-13 15:16:01 |
| torch-spline-conv |
1.2.2 |
Implementation of the Spline-Based Convolution Operator of SplineCNN in PyTorch |
2023-03-16 14:33:40 |
| dgllife |
0.3.1 |
DGL-based package for Life Science |
2022-12-21 13:16:23 |