Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition

Zhongyuan Lyu, Dong Xia & Yuan Zhang
We introduce a unified framework, formulated as general latent space models, to study complex higher-order network interactions among multiple entities. Our framework covers several popular models in recent network analysis literature, including mixture multi-layer latent space model and hypergraph latent space model. We formulate the relationship between the latent positions and the observed data via a generalized multilinear kernel as the link function. While our model enjoys decent generality, its maximum likelihood parameter estimation is...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.