OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning

[PDF] arxiv.org

Graph Domain Adaptation: Challenges, Progress and Prospects

B Shi, Y Wang, F Guo, B Xu, H Shen… - arXiv preprint arXiv …, 2024 - arxiv.org
As graph representation learning often suffers from label scarcity problems in real-world
applications, researchers have proposed graph domain adaptation (GDA) as an effective …
被引用次数：1 相关文章 所有 2 个版本
[PDF] arxiv.org

Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation

M Liu, Z Zhang, J Tang, J Bu, B He, S Zhou - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
相关文章 所有 2 个版本
[PDF] arxiv.org

Graph Transformers: A Survey

A Shehzad, F Xia, S Abid, C Peng, S Yu… - arXiv preprint arXiv …, 2024 - arxiv.org
Graph transformers are a recent advancement in machine learning, offering a new class of
neural network models for graph-structured data. The synergy between transformers and …
相关文章 所有 2 个版本