3 papers
cs.LG2024
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?
Qian Ma, Haitao Mao, Jingzhe Liu +5
Self-supervised learning~(SSL) is essential to obtain foundation models in NLP and CV domains via effectively leveraging knowledge in large-scale unlabeled data. The reason for its…
cs.LG2024
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
Xiaowei Qian, Zhimeng Guo, Jialiang Li +4
Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies o…
cs.LG2024
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
Qian Ma, Hongliang Chi, Hengrui Zhang +6
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community. This enthusiasm has l…