Title: Graph Encoder Embedding
Abstract: Graph data is a high-dimensional and structured data object that often requires proper dimension reduction prior to subsequent inference.
In this talk, I will introduce a recent graph embedding method called graph encoder embedding.
Comparing to existing methods, the encoder embedding is extremely fast and scalable, easy to visualize and interprete, and asymptotically consistent under popular random graph models.
I will illustrate its properties and usages via simulations and data applications on vertex classification and clustering.
Here is the zoom link is: https://wse.zoom.us/j/95448608570