4 papers · 1 filter
Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
Hongliang Chi, Qiong Wu, Zhengyi Zhou +1
Graph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes rem…
Precedence-Constrained Winter Value for Effective Graph Data Valuation
Hongliang Chi, Wei Jin, Charu Aggarwal +1
Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effe…
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…
Active Learning for Graphs with Noisy Structures
Hongliang Chi, Cong Qi, Suhang Wang +1
Graph Neural Networks (GNNs) have seen significant success in tasks such as node classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the exce…