Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently accurately learning features multiple graphs has important applications statistical inference on graphs. We propose method to jointly embed undirected Given set graphs, the joint embedding identifies linear subspace spanned by rank one symmetric matrices projects adjacency into this sub...

Since many real world networks are evolving over time, such as social and user-item networks, there increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of graphs but not only the latest network, for preserving both structural temporal information networks. Due to lack comprehensive investigation them, we give survey this pape...