yao8839836/text_gcn

Health v2 · maintenance only

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Adoption not part of the score

1389 stars · 438 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The official implementation of Text GCN, a Graph Convolutional Network approach for text classification published at AAAI 2019. It builds word-document graphs from corpora and trains a GCN to classify documents on standard benchmarks like 20NG, R8, R52, Ohsumed, and MR.

Use cases

• classify documents into categories using graph neural networks
• reproduce Text GCN results from the AAAI 2019 paper
• run text classification on benchmark datasets like 20 Newsgroups
• build word-document graphs from a text corpus
• experiment with GCNs for NLP tasks
• compare graph-based text classification against baselines

When to choose

• you want to reproduce or build on the Text GCN paper's approach
• you need graph-based document classification on small-to-medium benchmark corpora
• you are researching graph neural networks for NLP

When to avoid

• you need a production-ready, maintained text classification pipeline
• you want modern TensorFlow 2.x or PyTorch support
• you need inductive classification on unseen documents (use fast_text_gcn instead)
• you require a permissive license for commercial use

Facets

library · maturity maintenance

machine-learning deep-learning nlp machine-learning deep-learning python graph-convolutional-networks text-classification tensorflow research-code aaai-2019 natural-language-processing

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