mdeff/cnn_graph

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1366 stars · 386 forks observed · 2026-08-28

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

Reference implementation of the NIPS 2016 paper 'Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering' (ChebNet), generalizing CNNs to arbitrary graphs via spectral graph theory. It ships TensorFlow code and Jupyter notebooks that reproduce the MNIST and 20NEWS experiments and demonstrate applying the graph ConvNet to custom data.

Use cases

• run graph convolutional neural networks on graph-structured data
• reproduce the NIPS 2016 ChebNet paper experiments on MNIST and 20NEWS
• classify samples with an adjacency matrix encoding graph structure
• learn localized spectral filters on irregular domains like social networks or connectomes
• study implementations of spectral networks and deep convolutional nets on graphs

When to choose

• you want the original Chebyshev graph convolution implementation from the NIPS 2016 paper
• you have data with an explicit graph/adjacency structure and want a simple TensorFlow 1.x model
• you are studying or teaching graph signal processing and graph neural networks

When to avoid

• you need a maintained, production-grade GNN library (the repo targets TensorFlow 1.x and sees little activity)
• you want modern GNN architectures like GCN, GAT, or GraphSAGE with active support
• you work in PyTorch or need GPU-optimized large-scale training

Facets

library · maturity maintenance

deep-learning machine-learning deep-learning machine-learning graph-processing python graph-neural-networks graph-convolutional-networks spectral-filtering tensorflow jupyter-notebooks research-code nips-2016

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