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cs.LG2026
Uncertainty Quantification on Graph Learning: A Survey
Chao Chen, Chenghua Guo, Rui Xu +6
Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the…
cs.LG2025
Graph Neural Architecture Search with GPT-4
Haishuai Wang, Yang Gao, Xin Zheng +3
Graph Neural Architecture Search (GNAS) has shown promising results in finding the best graph neural network architecture on a given graph dataset. However, existing GNAS methods s…
cs.LG2025
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Zheng Wang, Hongming Ding, Li Pan +3
Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph…