9 papers
Unfolding and Fusion: Debiased Inference for Generalized Multilayer Latent Space Models
Zhaozhe Liu, Gongjun Xu, Haoran Zhang
Multilayer networks have become increasingly ubiquitous across diverse scientific fields, yet their inferential theory remains underdeveloped. We propose a flexible latent space mo…
Identifiability and Inference for Generalized Latent Factor Models
Chengyu Cui, Gongjun Xu
Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning, but also serves as a widely used tool across various sci…
Theoretical Analysis of Engression and Reverse Markov Engression
Jiaqi Huang, Gongjun Xu, Ji Zhu
Engression is a recently proposed and effective framework for conditional distribution learning. Its multi-step Reverse Markov extension further improves generative flexibility by…
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
Feifan Jiang, Yinan Bu, Shihao Wu +2
Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation…
Denoising Diffused Embeddings: a Generative Approach for Hypergraphs
Shihao Wu, Junyi Yang, Gongjun Xu +1
Hypergraph data, which capture multi-way interactions among entities, are increasingly prevalent in the big data era. Generating new hyperlinks from an observed, usually high-dimen…
Hyperbolic Network Latent Space Model with Learnable Curvature
Jinming Li, Gongjun Xu, Ji Zhu
Network data is ubiquitous in various scientific disciplines, including sociology, economics, and neuroscience. Latent space models are often employed in network data analysis, but…