To encode the neighbor information into each node's representation, we follow the propagation rule defined in Graph Convolution Network (GCN) (Kipf and Welling, 2016).
with the adjacent or co-occurrence matrix A, we define the following propagation rule on W o : Here T ∈ R d f ×d f is the transformation matrix and I N is an identity matrix used to add selfconnected edges.D is a diagonal degree matrix withD ii = jÃ ij , which is used to normalize the feature vectors such that the number of neighbors does not affect the scale of transformed feature vectors.