Table 6 Cross-domain graph classification on Letter-Drawing dataset.

From: TO-UGDA: target-oriented unsupervised graph domain adaptation

Methods

Low\(\rightarrow\)Med

Low\(\rightarrow\)High

Med\(\rightarrow\)Low

Med\(\rightarrow\)High

High\(\rightarrow\)Low

High\(\rightarrow\)Med

Avg.

GCN59

0.4822

0.2982

0.6382

0.2800

0.5391

0.3884

0.4377

SGC60

0.5253

0.2160

0.7921

0.2908

0.5346

0.3986

0.4596

GIN44

0.4973

0.2102

0.7812

0.2849

0.5049

0.4102

0.4481

CDAN35

0.4012

0.2367

0.6587

0.2154

0.5071

0.4483

0.4112

ToAlign65

0.5829

0.2570

0.6374

0.2714

0.5946

0.4768

0.4700

MetaAlign66

0.5271

0.2638

0.6819

0.2932

0.5623

0.5122

0.4734

DEAL24

0.5731

0.3189

0.7413

0.2911

0.6136

0.5587

0.5161

COCO48

0.5965

0.3347

0.7893

0.3149

0.7445

0.5947

0.5624

TO-UGDA

0.6212

0.3473

0.8526

0.3355

0.8318

0.6139

0.6004