Adapting Graph Models via Target Integrity Assessment and Source Distribution Hypothesis.

Area of Science:

• Graph Machine Learning
• Artificial Intelligence
• Computer Science

Background:

• Graph transfer learning (GTL) adapts models across graph datasets.
• Traditional GTL requires source graph access, limiting its use in privacy-sensitive scenarios.
• Source-free domain adaptation (SFDA) addresses GTL without source data.

Purpose of the Study:

• To develop a novel SFDA method for graph transfer learning.
• To enable effective adaptation of pretrained graph models to target graphs without source data access.
• To enhance the flexibility and applicability of GTL in real-world scenarios.

Main Methods:

• Incorporation of a weighted information maximization loss with posterior integrities of target nodes.
• Estimation of source graph distributions and synthesis of source nodes.
• Utilizing a reconstruction decoder for synthesized node authenticity and adversarial learning for distribution alignment.

Main Results:

• The proposed SFDA method significantly improves graph model adaptation performance.
• Experimental results demonstrate superior performance over existing state-of-the-art methods.
• The approach effectively handles target graphs with distributions different from the source graph.

Conclusions:

• SFDA is a viable and effective approach for graph transfer learning in source-unavailable settings.
• The developed method offers a flexible and powerful solution for adapting graph models.
• This research advances the field of GTL by enabling adaptation without compromising data privacy or security.