5 papers
Fused Spatial Latent Block Models for Co-Clustering
Biao Cai, Yuanxing Chen, Kuangnan Fang +1
Spatial transcriptomics is a rapidly growing technique that captures gene expression together with spatial coordinates in intact tissue sections, enabling in situ mapping of transc…
Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data
Jialei Liu, Jun Liao, Kuangnan Fang
Positive-Unlabeled (PU) learning presents unique challenges due to the lack of explicitly labeled negative samples, particularly in high-stakes domains such as fraud detection and…
Common-Individual Embedding for Dynamic Networks with Temporal Group Structure
Hairi Bai, Xinyan Fan, Kuangnan Fang +1
We propose STANE (Shared and Time-specific Adaptive Network Embedding), a new joint embedding framework for dynamic networks that captures both stable global structures and localiz…
Transfer learning under latent space model
Kuangnan Fang, Ruixuan Qin, Xinyan Fan
Latent space model plays a crucial role in network analysis, and accurate estimation of latent variables is essential for downstream tasks such as link prediction. However, the lar…
Network Model Averaging Prediction for Latent Space Models by K-Fold Edge Cross-Validation
Yan Zhang, Jun Liao, Xinyan Fan +2
In complex systems, networks represent connectivity relationships between nodes through edges. Latent space models are crucial in analyzing network data for tasks like community de…