SGL: Scalable Graph Learning¶
SGL is a Graph Neural Network (GNN) toolkit targeting scalable graph learning, which supports deep graph learning on extremely large datasets. SGL allows users to easily implement scalable graph neural networks and evaluate its performance on various downstream tasks like node classification, node clustering, and link prediction. Further, SGL supports auto neural architecture search functionality based on OpenBox. SGL is designed and developed by the graph learning team from the DAIR Lab at Peking University.
Library Highlights¶
High scalability: Follow the scalable design paradigm SGAP in PaSca, SGL scale to graph data with billions of nodes and edges.
Auto neural architecture search: Automatically choose decent neural architectures according to specific tasks, and pre-defined objectives (e.g., inference time).
Ease of use: User-friendly interfaces of implementing existing scalable GNNs and executing various downstream tasks.
License¶
The entire codebase is under MIT license.
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API Reference