Graph Domain Adaptation: Challenges, Progress and Prospects
As graph representation learning often suffers from label scarcity problems in real-world
applications, researchers have proposed graph domain adaptation (GDA) as an effective …
applications, researchers have proposed graph domain adaptation (GDA) as an effective …
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a
label-rich source graph to an unlabeled target graph under domain discrepancies. Despite …
label-rich source graph to an unlabeled target graph under domain discrepancies. Despite …
Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs
S Liu, K Ding - arXiv preprint arXiv:2402.11153, 2024 - arxiv.org
Distribution shifts on graphs--the data distribution discrepancies between training and
testing a graph machine learning model, are often ubiquitous and unavoidable in real-world …
testing a graph machine learning model, are often ubiquitous and unavoidable in real-world …
Tackling Negative Transfer on Graphs
Transfer learning aims to boost the learning on the target task leveraging knowledge
learned from other relevant tasks. However, when the source and target are not closely …
learned from other relevant tasks. However, when the source and target are not closely …
[PDF][PDF] Can Modifying Data Address Graph Domain Adaptation?
Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph
analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to …
analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to …