Graph Neural Networks in Python

Multiplying the weights with the adjacency matrix means that all the feature vectors of all (1-hop) neighboring nodes are summed and aggregated for every node. However, the feature vector of the node itself is not included. To address this, Kipf and Welling [4] add the identity matrix to the adjacency matrix and denote this new matrix  A I. Multiplication of the adjacency matrix will also change the scale of the feature vectors.