Graph neural networks (GNNs) have achieved remarkable success across a wide
range of applications, such as recommendation, drug discovery, and question
answering. Behind the success of GNNs lies the backpropagation (BP) algorithm,
which is the de facto standard for training deep neural networks (NNs).
However, despite its effectiveness, BP imposes several constraints, which are
not only biologically implausible, but also limit the scalability, parallelism,
and flexibility in learning NNs. Examples of such constraints include storage
of neural activities computed in the forward pass for use in the subsequent
backward pass, and the dependence of parameter updates on non-local signals. To
address these limitations, the forward-forward algorithm (FF) was recently
proposed as an alternative to BP in the image classification domain, which
trains NNs by performing two forward passes over positive and negative data.
Inspired by this advance, we propose ForwardGNN in this work, a new forward
learning procedure for GNNs, which avoids the constraints imposed by BP via an
effective layer-wise local forward training. ForwardGNN extends the original FF
to deal with graph data and GNNs, and makes it possible to operate without
generating negative inputs (hence no longer forward-forward). Further,
ForwardGNN enables each layer to learn from both the bottom-up and top-down
signals without relying on the backpropagation of errors. Extensive experiments
on real-world datasets show the effectiveness and generality of the proposed
forward graph learning framework. We release our code at
this https URL

通过本文中提出的 ForwardGNN 算法，我们可以以一种新的前向学习方式来解决 BP 算法所带来的局限性，该算法扩展了原始的前向传播算法，使其适用于图数据和图神经网络，并且不再需要产生负输入数据，同时每个层可以从自下而上和自上而下的信号中学习，而不依赖于误差的后向传播。在实际数据集上进行的大量实验表明，这种前向图学习框架的有效性和普适性。