Graph Datasets. Real-world graphs from Stanford’s Large Network Dataset Co
Real-world graphs from Stanford’s Large Network Dataset Collection (https://snap. 0 dgl>=1. OGB aims to provide graph datasets that cover important graph machine learning tasks, diverse dataset scale, and rich domains. The NCI graph datasets are commonly used as the benchmark for graph classification. This page contains collected benchmark datasets for the evaluation of graph kernels and graph neural networks. 12 torch_geometric>=2. This package supplies management and utilities for graph datasets used to train graph neural networks and more specifically aimed at explainable AI OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, The largest repository of network datasets First Blockchain ML-Ready Dataset Platform A comprehensive, open-science catalogue of machine learning-ready blockchain graph datasets. . Click on the names of the collections to expand them and access information torch_geometric. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million Working with Graph Datasets Creating Graph Datasets Loading Graphs from CSV Dataset Splitting Use-Cases & Applications Distributed Training Download Open Datasets on 1000s of Projects + Share Projects on One Platform. stanford. Awesome graph-level learning methods. Graph ML Tasks: We cover three fundamental A collection of benchmark datasets, data-loaders and evaluators for graph machine learning in PyTorch. Each NCI dataset belongs to a bioassay task for anticancer activity prediction, where each chemical A variety of artificially and semi-artificially generated graph datasets from the "Benchmarking Graph Neural Networks" paper. 8 torch>=1. Explore Popular Topics Like Government, Sports, Medicine, CoDEx: A set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia - tsafavi/codex The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. This includes social network data, brain networks, To reach this goal, we release GraphGT, a systematic graph generation and transformation dataset collection. datasets Contents Homogeneous Datasets Heterogeneous Datasets Hypergraph Datasets Synthetic Datasets Graph Generators Motif Generators Homogeneous Datasets Also share and contribute by uploading recent network data sets. OGB About 16k of clean images of graphs About A list of graph datasets for machine learning projects including Graph Neural Networks. edu/data/) as well as synthetic data at various scales generated using Working with Graph Datasets Creating Graph Datasets Loading Graphs from CSV Dataset Splitting We list the well-known open graph datasets in Table 1. The dataset combines scale-aligned real-world graphs and synthetic graphs, offering a versatile resource for evaluating various graph-related technologies. This includes social network data, brain networks, Database dump files In the GitHub repository Neo4j graph examples, you find dump files for several graph example datasets, including the ones listed Project description Graph Datasets Installation python>=3. We collect, process, and open-source diverse types of graph-structured GitHub is where people build software. Collections of commonly used datasets, papers as well as implementations are listed in Graph Benchmark DatasetsThe following is a list of benchmark datasets for testing graph layout algorithms. 1 $ python -m pip install graph_datasets Usage See Graph Also share and contribute by uploading recent network data sets. These datasets have provided different explicit categorizations to support various research purposes. Naturally all conceivable data may be represented as a graph for analysis. The citation network datasets "Cora", "CiteSeer" and The goal of this repository is to store the different graph datasets currently available as benchmarks, to provide them in an homogeneous and easily loadable way.
