The edge network, which passes messages from 1-hop neighbors w^{t}_{i} of v^{t} to v^{t}, based on the edge features between them (e_{v^{t}w^{t}_{i}}, where t = 0), resulting in an updated node state v^{t+1}. _{i} denotes the i:th neighbor of v^{t} and ^{t} the t:th state of v or w. An important feature of the edge network (in contrast to e.g. the relational graph convolutional network) is that it allows for non-discrete edge features. However, in this tutorial, only discrete edge features will be used.