We explore distinct strategies for neighborhood aggregation, adopting three GNNs: Gated Graph Neural Networks GGNNs employ gated recurrent units to encode node representations, reducing the recurrence to a fixed number of steps.
the l-th layer of a GGNN is calculated as: where N (i) is the immediate neighborhood of v i , W 1 is a parameter and GRU is a gated recurrent unit (Cho et al., 2014).