We present the Graph Forward-Forward (GFF) algorithm, an extension of the
Forward-Forward procedure to graphs, able to handle features distributed over a
graph's nodes. This allows training graph neural networks with forward passes
only, without backpropagation. Our method is agnostic to the message-passing
scheme, and provides a more biologically plausible learning scheme than
backpropagation, while also carrying computational advantages. With GFF, graph
neural networks are trained greedily layer by layer, using both positive and
negative samples. We run experiments on 11 standard graph property prediction
tasks, showing how GFF provides an effective alternative to backpropagation for
training graph neural networks. This shows in particular that this procedure is
remarkably efficient in spite of combining the per-layer training with the
locality of the processing in a GNN.

本文介绍了图正向正向（GFF）算法，它是一种扩展的前向前向算法，适用于处理分布在图节点上的特征，可以使用正向传递来训练图神经网络，而无需反向传输；其通过贪婪、逐层的方式将图神经网络层层训练，并使用正负样本，显示出比反向传播更加高效的计算优势。