4 papers
Informative Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
The quality of graph-structured data is fundamental to the success of modern graph analysis techniques such as Graph Neural Networks (GNNs). However, real-world graph data is often…
Uncertainty-Aware Graph Structure Learning
Shen Han, Zhiyao Zhou, Jiawei Chen +6
Graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph…
Motif-driven Subgraph Structure Learning for Graph Classification
Zhiyao Zhou, Sheng Zhou, Bochao Mao +5
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstr…
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Zhiyao Zhou, Sheng Zhou, Bochao Mao +7
Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node at…