Collective Representation Learning on Spatiotemporal Heterogeneous Information Networks
Dakshak Keerthi Chandra, Pengyang Wang, Jennifer L. Leopold, Yanjie Fu · 2019
Representation learning is a technique that is used to capture the underlying latent features of complex data. Representation learning on networks has been widely implemented for learning network structure and embedding it in a low dimensional vector space. In recent years, network embedding using representation learning has attracted increasing attention, and many deep architectures have been widely proposed. However, existing network embedding techniques ignore the multi-class spatial and temporal relationships that crucially reflect the complex nature among vertices and links in spatiotemporal heterogeneous information networks(SHINs).