Semi-supervised Domain Adaptation in Graph Transfer Learning
- URL: http://arxiv.org/abs/2309.10773v1
- Date: Tue, 19 Sep 2023 17:20:58 GMT
- Title: Semi-supervised Domain Adaptation in Graph Transfer Learning
- Authors: Ziyue Qiao, Xiao Luo, Meng Xiao, Hao Dong, Yuanchun Zhou, and Hui
Xiong
- Abstract summary: Unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs.
This imposes critical challenges on graph transfer learning due to serious domain shifts and label scarcity.
We propose a method named Semi-supervised Graph Domain Adaptation (SGDA) to address these challenges.
- Score: 24.32465362708831
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As a specific case of graph transfer learning, unsupervised domain adaptation
on graphs aims for knowledge transfer from label-rich source graphs to
unlabeled target graphs. However, graphs with topology and attributes usually
have considerable cross-domain disparity and there are numerous real-world
scenarios where merely a subset of nodes are labeled in the source graph. This
imposes critical challenges on graph transfer learning due to serious domain
shifts and label scarcity. To address these challenges, we propose a method
named Semi-supervised Graph Domain Adaptation (SGDA). To deal with the domain
shift, we add adaptive shift parameters to each of the source nodes, which are
trained in an adversarial manner to align the cross-domain distributions of
node embedding, thus the node classifier trained on labeled source nodes can be
transferred to the target nodes. Moreover, to address the label scarcity, we
propose pseudo-labeling on unlabeled nodes, which improves classification on
the target graph via measuring the posterior influence of nodes based on their
relative position to the class centroids. Finally, extensive experiments on a
range of publicly accessible datasets validate the effectiveness of our
proposed SGDA in different experimental settings.
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