70 citations · 100 across the 3 of their papers we have counts for
3 papers
cs.AI2026
Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
Rawaa Alatrash, Mohamed Amine Chatti, Hong Yang +1
User modeling is a critical task in a variety of personalized systems. Recognizing their effectiveness in learning from graph-structured data, Graph Neural Networks (GNNs), particu…
cs.LG2019★ 30 cited
Deep Active Learning for Anchor User Prediction
Anfeng Cheng, Chuan Zhou, Hong Yang +4
Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consi…
cs.LG2019★ 70 cited
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
Yang Gao, Hong Yang, Peng Zhang +2
Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph n…