{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"OGB provides standardized benchmark datasets, data loaders, and evaluators for graph machine learning tasks across node, link, and graph prediction problems.","skillfed_tags":["graph-neural-networks","benchmark-datasets","ml-evaluation"],"use_cases":["Benchmark a new graph neural network architecture against standardized datasets and compare results using OGB's unified evaluators.","Download and prepare graph datasets with automatic splitting for training node, link, or graph classification models.","Evaluate graph ML methods on datasets spanning different scales and domains without manual preprocessing.","Reproduce published results on OGB benchmarks to validate research or baseline implementations.","Access diverse real-world graphs from scientific, social, and knowledge-graph domains for algorithm development."],"what_it_does":"OGB is a collection of standardized benchmark datasets and tools for evaluating graph machine learning methods. It provides easy-to-use data loaders compatible with popular graph deep learning frameworks, handling dataset downloading, standardized train/validation/test splits, and unified evaluation metrics. The package covers three core graph ML tasks\u2014node prediction, link prediction, and graph prediction\u2014across datasets of varying scales (from single-GPU to multi-GPU) and diverse domains including scientific networks, social networks, and heterogeneous knowledge graphs.\n\nThe package is designed to enable reliable comparison of different graph neural network methods by providing consistent dataset preparation and evaluation protocols. Researchers use OGB to benchmark their models against established baselines on real-world graph problems, avoiding the friction of manual dataset curation and metric implementation.","worth_installing":"Yes, if you are working on graph neural networks and need standardized benchmarks. The package is well-established with 2093 GitHub stars, has no known vulnerabilities, and provides genuine value through unified data loading and evaluation. However, maintenance is aging (last release 2023-04-07)\u2014verify compatibility with your PyTorch and graph framework versions before relying on it for new research."},"id":"ogb","links":{"html":"https://skillfed.io/packages/ogb","md":"https://skillfed.io/packages/ogb.md","pypi":"https://pypi.org/project/ogb/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2023-04-07","license_spdx":null,"license_treatment":"permissive","name":"ogb","python_support":"unspecified","summary":"Open Graph Benchmark"},"popularity":{"monthly_downloads":115963,"position":12227,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.6"}