3 papers
cs.LG2024
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Yige Zhao, Jianxiang Yu, Yao Cheng +4
Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existi…
cs.LG2024
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
Chenghua Gong, Xiang Li, Jianxiang Yu +3
Graphs have become an important modeling tool for web applications, and Graph Neural Networks (GNNs) have achieved great success in graph representation learning. However, the perf…
cs.LG2024
Class-Balanced and Reinforced Active Learning on Graphs
Chengcheng Yu, Jiapeng Zhu, Xiang Li
Graph neural networks (GNNs) have demonstrated significant success in various applications, such as node classification, link prediction, and graph classification. Active learning…