Given network G, with the network embedding we seek to encode each vertex into a low-dimensional vector h (with dimension much smaller than N ), while preserving structural and semantic features of G. To incorporate both structural and semantic information into the network embeddings, we specify two types of (latent) embeddings: (i) h s , the structural embedding; and (ii) h t , the textual embedding.
each vertex in G is encoded into a low-dimensional embedding h = [h s ; h t ].