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gtrick is an easy-to-use Python package collecting tricks for Graph Neural Networks. It tests and provides powerful tricks to boost your models' performance.

Trick is all you need! (中文简介)

Library Highlights

• Easy-to-use: All it takes is to add a few lines of code to apply a powerful trick, with as little changes of existing code as possible.

• Verified Trick: All tricks implemented in gtrick are tested on our selected datasets. Only the tricks indeed improving model's performance can be collected by gtrick.

• Backend Free: We provide all tricks both in DGL and PyG. Whatever graph learning library you use, feel free to try it.

Installation

Warning

This is a developmental release.

You can install gtrick by pip:

pip install gtrick

Usage Example

It is very easy to get start with gtrick. Suppose you have a model for node classification:

from torch_geometric.nn import GCNConv
from torch_geometric.datasets import TUDataset


dataset = TUDataset(name='ENZYMES')
data = dataset[0]
model = GCNConv(dataset.num_node_features, dataset.num_classes)

out = model(data.x, data.edge_index)

You can enhance your GNN model with only a few lines of code:

from torch_geometric.nn import GCNConv
from torch_geometric.datasets import TUDataset
from gtrick import random_feature

dataset = TUDataset(name='ENZYMES')
data = dataset[0]
model = GCNConv(dataset.num_node_features, dataset.num_classes)
h = random_feature(data.x)
out = model(h, data.edge_index)

Implemented Tricks

Trick	Example	Task	Reference
VirtualNode	DGL PyG	graph	OGB Graph Property Prediction Examples
FLAG	DGL PyG	node* graph	Robust Optimization as Data Augmentation for Large-scale Graphs
Fingerprint	DGL PyG	molecular graph*	Extended-Connectivity Fingerprints
Random Feature	DGL PyG	graph*	Random Features Strengthen Graph Neural Networks
Label Propagation	DGL PyG	node*	Learning from Labeled and Unlabeled Datawith Label Propagation
Correct & Smooth	DGL PyG	node*	Combining Label Propagation And Simple Models Out-performs Graph Neural Networks
Common Neighbors	DGL PyG	link*	Link Prediction with Structural Information
Resource Allocation	DGL PyG	link*	Link Prediction with Structural Information
Adamic Adar	DGL PyG	link*	Link Prediction with Structural Information
Anchor Distance	DGL PyG	link*	Link Prediction with Structural Information