{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"PyTorch Geometric is a library for building and training Graph Neural Networks (GNNs) on structured data, providing pre-built GNN layers, datasets, data loaders, and utilities for geometric deep learning.","skillfed_tags":["graph-neural-networks","geometric-deep-learning","pytorch-extension"],"use_cases":["Classify nodes in citation networks or social graphs using pre-built GCN or GraphSAGE layers.","Build custom GNN architectures by extending the MessagePassing base class for research or domain-specific problems.","Train on large-scale graphs with millions of nodes using mini-batch loaders and scalable GNN models.","Perform link prediction or graph-level classification on benchmark datasets (OGB, Cora, Citeseer, etc.).","Process 3D point cloud data or mesh structures using geometric transformations and specialized layers.","Prototype heterogeneous graph models with multiple node and edge types for knowledge graphs or recommendation systems."],"what_it_does":"PyTorch Geometric is a framework for implementing Graph Neural Networks on top of PyTorch. It provides a collection of pre-built GNN layers (GCNConv, GraphSAGE, GAT, etc.), a message-passing API for custom architectures, mini-batch loaders for both small and large graphs, and benchmark datasets. The library is designed to feel like native PyTorch\u2014if you know PyTorch, the API is straightforward.\n\nYou use it to solve problems on graph-structured data: node classification, link prediction, graph classification, and other geometric deep learning tasks. It handles diverse graph types including static graphs, dynamic graphs, heterogeneous graphs with multiple node and edge types, and 3D point clouds. The library supports multi-GPU training and torch.compile for performance optimization.","worth_installing":"Yes. PyTorch Geometric is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and is in the top 5000 PyPI packages by download volume. Install friction is low and the MIT license is permissive. It is the standard library for GNN work in PyTorch\u2014install it if you need to build or train graph neural networks."},"id":"torch-geometric","links":{"html":"https://skillfed.io/packages/torch-geometric","md":"https://skillfed.io/packages/torch-geometric.md","pypi":"https://pypi.org/project/torch-geometric/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-20","license_spdx":"MIT","license_treatment":"permissive","name":"torch-geometric","python_support":"supports_current","summary":"Graph Neural Network Library for PyTorch"},"popularity":{"monthly_downloads":1517719,"position":3821,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.8.0.post1"}