Hybrid Low-Order and Higher-Order Graph Convolutional Networks

Algorithm 1

Iterative calculation for HLHG-2.

(1)	Inputs: , , and the other parameters. N (number of hidden units), dr (dropout rate), L2 (L2 regularization), es (early stopping), epochs and lr (learning rate).
	Output: weight parameters and .
(2)	Randomly generate the trainable weights and ;
(3)	Iteratively calculate the forward output value(1), (2)(3);(4), (5)(6)(4)Calculate the cross entropy