The goal of the social attention model is to assign similar basis weights to authors who are nearby in the social network G. We operationalize social proximity by embedding each node's social network position into a vector representation.
we employ the LINE method (Tang et al., 2015b), which estimates D (v) dimensional node embeddings v a as parameters in a probabilistic model over edges in the social network.