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Bag of Tricks for Graph Neural Networks.
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| 2026-07-31 | 293 |
| 2026-08-04 | 293 |
| 2026-08-05 | 292 |
| 2026-08-06 | 292 |
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# gtrick: Bag of Tricks for Graph Neural Networks.      gtrick is an easy-to-use Python package that collects tricks for graph neural networks. It tests and provides powerful tricks to boost your models' performance. Trick is all you need! ([English Document](https://sangyx.com/gtrick/) | [中文介绍](https://zhuanlan.zhihu.com/p/508876898)) ## Library Highlights * **Easy-to-use**: All it takes is to add a few lines of code to apply a powerful trick, with as few changes to existing code as possible. * **Verified Trick**: All tricks implemented in gtrick are tested on our selected datasets. Only the tricks indeed improving a model's performance can be collected by gtrick. * **Backend Free**: We provide all tricks both in [DGL](https://www.dgl.ai/) and [PyG](https://www.pyg.org/). Whatever graph learning library you use, feel free to try it. ## Installation *Note: This is a developmental release.* ```bash pip install gtrick ``` ## Quick Start It is very easy to get started with gtrick. You can enhance your GNN model with only a few lines of code.  For more detailed examples, see Example in [Trick](#trick). ## Trick | Trick | Example | Task | Reference | |:------------:|:------------:|:------------:|:-----:| | VirtualNode | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/VirtualNode.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/VirtualNode.ipynb) | graph | [OGB Graph Property Prediction Examples](https://github.com/snap-stanford/ogb/tree/master/examples/graphproppred/mol) | | FLAG | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/FLAG.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/FLAG.ipynb) | node*<br>graph | [Robust Optimization as Data Augmentation for Large-scale Graphs](https://arxiv.org/abs/2010.09891) | | Fingerprint | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/Fingerprint.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/Fingerprint.ipynb) | molecular graph* | [Extended-Connectivity Fingerprints](https://pubs.acs.org/doi/10.1021/ci100050t) | | Random Feature | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/RandomFeature.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/RandomFeature.ipynb) | graph* | [Random Features Strengthen Graph Neural Networks](http://arxiv.org/abs/2002.03155) | | Label Propagation | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/LabelProp.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/LabelProp.ipynb) | node* | [Learning from Labeled and Unlabeled Data with Label Propagation](http://mlg.eng.cam.ac.uk/zoubin/papers/CMU-CALD-02-107.pdf) | | Correct & Smooth | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/C&S.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/C&S.ipynb) | node* | [Combining Label Propagation And Simple Models Out-performs Graph Neural Networks](https://arxiv.org/abs/2010.13993) | | Common Neighbors | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/EdgeFeat.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/EdgeFeat.ipynb) | link* | [Link Prediction with Structural Information](https://github.com/lustoo/OGB_link_prediction/blob/main/Link%20prediction%20with%20structural%20information.pdf) | | Resource Allocation | [DGL](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/dgl/EdgeFeat.ipynb)<br>[PyG](https://nbviewer.org/github/sangyx/gtrick/blob/main/benchmark/pyg/EdgeFeat.ipynb) | l
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:82c2f706c4c1685c, llm:Repository description: 'Bag of Tricks for Graph Neural Networks'. Topics: dgl, graph-neural-networks, torch-geometric.
matched fp:82c2f706c4c1685c, llm:Repository description: 'Bag of Tricks for Graph Neural Networks'. Topics: dgl, graph-neural-networks, torch-geometric.
matched fp:82c2f706c4c1685c, llm:Repository description: 'Bag of Tricks for Graph Neural Networks'. Topics: dgl, graph-neural-networks, torch-geometric.