Thus we assign different weights to information from different types of edges to integrate such information.
we concatenate the learned representations from all types of edges and perform a linear transformation, mathematically represented as: hidden is the weight matrix and T is the size of the edge type vocabulary and d hidden is the hidden dimension in DCGCN layers as described in Section 2.2. b f ∈ R d hidden is a bias vector.