PyGDA is a Python library for Graph Domain Adaptation built upon PyTorch and PyG to easily train graph domain adaptation models in a sklearn style. PyGDA includes 20+ graph domain adaptation models. See examples with PyGDA below!

Graph Domain Adaptation Using PyGDA with 5 Lines of Code

from pygda.models import A2GNN

# choose a graph domain adaptation model
model = A2GNN(in_dim=num_features, hid_dim=args.nhid, num_classes=num_classes, device=args.device)

# train the model
model.fit(source_data, target_data)

# evaluate the performance
logits, labels = model.predict(target_data)

PyGDA is featured for:

• Consistent APIs and comprehensive documentation.
• Cover 20+ graph domain adaptation models.
• Scalable architecture that efficiently handles large graph datasets through mini-batching and sampling techniques.
• Seamlessly integrated data processing with PyG, ensuring full compatibility with PyG data structures.

📢 What's New?

[12/2024]. We now support source-free setting of graph domain adaptation.

• 3 recent models including GTrans, SOGA and GraphCTA are supported.

[08/2024]. We support graph-level domain adaptation task.

• 7 models including A2GNN, AdaGCN, CWGCN, DANE, GRADE, SAGDA, UDAGCN are supported.
• Various TUDatasets are supported including FRANKENSTEIN, Mutagenicity and PROTEINS.
• To perform a graph-level domain adaptation task, only one parameter is added to the model as follows:

model = A2GNN(in_dim=num_features, hid_dim=args.nhid, num_classes=num_classes, mode='graph', device=args.device)

Installation

Note: PyGDA depends on PyTorch, PyG, PyTorch Sparse and Pytorch Scatter. PyGDA does not automatically install these libraries for you. Please install them separately in order to run PyGDA successfully.

Required Dependencies:

• torch>=1.13.1
• torch_geometric>=2.4.0
• torch_sparse>=0.6.15
• torch_scatter>=2.1.0
• python3
• scipy
• sklearn
• numpy
• cvxpy
• tqdm

Installing with pip:

pip install pygda

or

Installation for local development:

git clone https://github.com/pygda-team/pygda
cd pygda
pip install -e .

Quick Start

Step 1: Load Data

from pygda.datasets import CitationDataset

source_dataset = CitationDataset(path, args.source)
target_dataset = CitationDataset(path, args.target)

Step 2: Build Model

from pygda.models import A2GNN

model = A2GNN(in_dim=num_features, hid_dim=args.nhid, num_classes=num_classes, device=args.device)

Step 3: Fit Model

model.fit(source_data, target_data)

Step 4: Evaluation

from pygda.metrics import eval_micro_f1, eval_macro_f1

logits, labels = model.predict(target_data)
preds = logits.argmax(dim=1)
mi_f1 = eval_micro_f1(labels, preds)
ma_f1 = eval_macro_f1(labels, preds)

Create your own GDA model

In addition to the easy application of existing GDA models, PyGDA makes it simple to implement custom models.

• the customed model should inherit BaseGDA class.
• implement your fit(), forward_model(), and predict() functions.

Reference

ID	Paper	Method	Venue
01	Semi-Supervised Classification with Graph Convolutional Networks	Vanilla GCN	ICLR 2017
02	DANE: Domain Adaptive Network Embedding	DANE	IJCAI 2019
03	Adversarial Deep Network Embedding for Cross-network Node Classification	ACDNE	AAAI 2020
04	Unsupervised Domain Adaptive Graph Convolutional Networks	UDAGCN	WWW 2020
05	Adversarial Separation Network for Cross-Network Node Classification	ASN	CIKM 2021
06	Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution	AdaGCN	TKDE 2022
07	Non-IID Transfer Learning on Graphs	GRADE	AAAI 2023
08	Graph Domain Adaptation via Theory-Grounded Spectral Regularization	SpecReg	ICLR 2023
09	Structural Re-weighting Improves Graph Domain Adaptation	StruRW	ICML 2023
10	Improving Graph Domain Adaptation with Network Hierarchy	JHGDA	CIKM 2023
11	Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer	KBL	CIKM 2023
12	Domain-adaptive Message Passing Graph Neural Network	DMGNN	NN 2023
13	Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation	CWGCN	TIP 2023
14	SA-GDA: Spectral Augmentation for Graph Domain Adaptation	SAGDA	MM 2023
15	Graph Domain Adaptation: A Generative View	DGDA	TKDD 2024
16	Rethinking Propagation for Unsupervised Graph Domain Adaptation	A2GNN	AAAI 2024
17	Pairwise Alignment Improves Graph Domain Adaptation	PairAlign	ICML 2024
18	Empowering Graph Representation Learning with Test-Time Graph Transformation	GTrans	ICLR 2023
19	Source Free Unsupervised Graph Domain Adaptation	SOGA	WSDM 2024
20	Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation	GraphCTA	WWW 2024

Cite

If you compare with, build on, or use aspects of PyGDA, please consider citing "Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation":

@inproceedings{liu2024revisiting,
title={Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation},
author={Meihan Liu and Zhen Zhang and Jiachen Tang and Jiajun Bu and Bingsheng He and Sheng Zhou},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=ZsyFwzuDzD}
}