Graph4NLP is an open-source library that makes it easy to apply Graph Neural Networks to natural language processing tasks. With about 1,700 stars on GitHub and releases through 2024, it is built on PyTorch and maintained by the Graph4AI community, with a companion website collecting tutorials and learning resources for the field.
The library provides a full pipeline for graph-powered NLP: graph construction from text, a rich set of graph neural network layers, and ready-made models for tasks such as semantic parsing, text classification, summarization and question answering. It supports multiple graph types as well as dynamic graph construction, so researchers can experiment with different ways of representing linguistic structure. Everything is packaged as a standard Python library installable from PyPI, with examples and documentation that lower the barrier to reproducing published results or prototyping new graph-based approaches to language problems, from knowledge-aware QA to graph-to-text generation.
The project also demonstrates its models on public benchmark datasets, giving newcomers working reference results to compare against when they reproduce the examples and tutorials on their own hardware for the first time.
Graph4NLP is free and open source under the Apache-2.0 license. There is no commercial version or hosted service; researchers and engineers simply install the library and run it on their own machines. Support is community-driven through GitHub issues and the project’s documentation.
NLP researchers experimenting with graph neural networks, graduate students reproducing GNN-for-NLP papers, and ML engineers who want structured graph features inside language pipelines. It assumes comfort with PyTorch and research-style code rather than offering a no-code workflow.