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Found 4 papers, 4 papers with code
Graph Domain Adaptation: Challenges, Progress and Prospects
To the best of our knowledge, this paper is the first survey for graph domain adaptation.
Batch-Mix Negative Sampling for Learning Recommendation Retrievers
Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss.
Causality and Independence Enhancement for Biased Node Classification
Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias.
OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning
Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models.
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