Search Results for author:
Found 4 papers, 0 papers with code
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
Active Learning for Graphs with Noisy Structures
Yet, the excessive cost of labeling large-scale graphs led to a focus on active learning on graphs, which aims for effective data selection to maximize downstream model performance.
Precedence-Constrained Winter Value for Effective Graph Data Valuation
Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation.
Enhancing Graph Contrastive Learning with Node Similarity
Graph contrastive learning (GCL) is a representative framework for self-supervised learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
