dsgiitr/graph_nets
PyTorch implementations and explanations of major graph representation learning papers including GCN, GraphSAGE, GAT, ChebNet, and DeepWalk.
Not currently ranked — collecting fresh signals.
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This repository provides Jupyter Notebook implementations of classic graph neural network papers as a supplement to an accompanying blog series. It covers DeepWalk for node embeddings, Graph Convolutional Networks for semi-supervised learning, GraphSAGE for inductive node classification, ChebNet using Chebyshev polynomials for spectral convolution, and Graph Attention Networks with attention mechanisms for graph representation learning.
Frequently asked
- What is dsgiitr/graph_nets?
- PyTorch implementations and explanations of major graph representation learning papers including GCN, GraphSAGE, GAT, ChebNet, and DeepWalk.
- Is graph_nets open source?
- Yes — dsgiitr/graph_nets is an open-source project tracked on heatdrop.
- What language is graph_nets written in?
- dsgiitr/graph_nets is primarily written in Jupyter Notebook.
- How popular is graph_nets?
- dsgiitr/graph_nets has 1.2k stars on GitHub.
- Where can I find graph_nets?
- dsgiitr/graph_nets is on GitHub at https://github.com/dsgiitr/graph_nets.