Pairwise Alignment Improves Graph Domain Adaptation (Pair-Align)

This repository is the implementation for the paper Pairwise Alignment Improves Graph Domain Adaptation by Shikun Liu, Deyu Zou, Han Zhao and Pan Li.

Overview

This work studies distribution shifts in real-world graph data for graph domain adaptation (GDA). In particular, we focus on the graph structure shift and decompose it into conditional structure shift (CSS) and label shift (LS). We propose a novel pairwise alignment (Pair-Align) algorithm to mitigate the CSS and LS simultaneously.

Figure 1. We illustrate structure shifts in real-world datasets. More details and statistics can be found in the paper.

The key idea of Pair-Align is to estimate and subsequently mitigate the distribu- tion shift in the neighboring nodes’ representations for any given node class c. To achieve this, Pair-Align employs a bootstrapping technique to recalibrate the influence of neighboring nodes in the message aggregation phase of GNNs. This strategic reweighting is key to effectively countering CSS. Concurrently, Pair-Align calculates label weights to alleviate disparities in the label distribution between source and target domains (addressing LS) by adjusting the classification loss.

The dataset files should be stored in the dataset_files folder. The codes to process the datasets are in the data_process folder. The codes for training, models, utils are in the src folder.

Datasets

• The DBLP/ACM datasets are adopted from the UDAGCN paper and can be downloaded following the Github repo

• The Arxiv dataset is from the ogbn-arxiv dataset and the processed file is adopted from EERM.

• The MAG dataset is originally from the ogbn-mag dataset and our code to process into separate graphs in different country is ./data_process/gen_spr_graph.py. The files needed to generate the processed graph and the saved graphs for the top 6 countries can be downloaded here.

• The raw HEP pileup dataset can be downloaded here, same as the StruRW paper. Then, the processed graph can be generated by running ./data_process/prepare_pileup.py.

Training

To run the codes, you can use the following commands. For all commands, the argument edge_rw indicates whether we employ $\gamma$ as edge weights for CSS mitigation and the argument label_rw indicates whether we use $\beta$ as label weights for LS mitigation.

The PA-CSS in the paper corresponds to edge_rw = True and label_rw = False

The PA-LS in the paper corresponds to edge_rw = False and label_rw = True

The PA-BOTH in the paper corresponds to edge_rw = True and label_rw = True

For the synthetic CSBM dataset:

python train.py --dataset CSBM --CSBM_set 'cond1' --valid_metric acc --edge_rw True --label_rw True --gnn_dim 20 --cls_dim 20

where the CSBM_set as cond1 and cond2 refers to the only class ratio shift scenarios. card1 and card2 refers to the only degree shift scenarios. css1 and css2 refers to shift in both. gss1 and gss2 refers to the joint shift in CSS and LS.

For the MAG dataset:

python train.py --dataset MAG --src_name 'US' --tgt_name 'CN' --valid_metric acc --edge_rw True --label_rw True --gnn_dim 300 --cls_dim 300

where the src_name can be chosen from US, CN and the tgt_name can be chosen from CN/US, DE, FR, RU, JP.

For the HEP pileup dataset:

python train.py --dataset Pileup --train_sig 'gg' --test_sig 'gg' --train_PU 10 --test_PU 50 --valid_metric f1 --edge_rw True --label_rw True --gnn_dim 50 --cls_dim 50

where the train_sig and test_sig can be chosen from gg, qq and the train_PU and test_PU can be chosen from 10, 30, 50, 140 from the setting introduced in the paper.

For the Arxiv dataset:

python train.py --dataset Arxiv --src_end_year 2007 --start_year 2014 --end_year 2016 --valid_metric acc --edge_rw True --label_rw True --gnn_dim 300 --cls_dim 300

where the src_end_year can be chosen from 2007, 2009, 2011 as indicating the source graph contains paper until which year. For target graph, we set the start_year from 2014, 2016 and end_year from 2016, 2018.

For the DBLP/ACM dataset:

python train_da.py --dataset DBLP_ACM --src_name acm --tgt_name dblp --valid_metric acc --edge_rw True --label_rw True --gnn_dim 128 --cls_dim 40

where the src_name can be chosen from DBLP, ACM and the tgt_name can be chosen from ACM, DBLP.

Some other hyperparameters

• --gamma_reg is to specify the $\delta$ value to control the robustness of $\gamma$ calculation

• --ls_lambda is to specify the $\lambda_w$ value to control regularization of $w$ in least square optimization

• --lw_lambda is to specify the $\lambda_\beta$ value to control regularization of $\beta$ in least square optimization

The detailed range and value of these hyperparameters for each dataset are listed in the paper appendix.