Graph convolutional networks (GCNs) have been successfully applied to node representation learning in various real-world applications. However. the performance of GCNs drops rapidly when the labeled data are severely scarce. and the node features are prone to being indistinguishable with stacking more layers. https://jcjxwzrzae1rm0.timeblog.net/70682614/the-use-of-plant-waste-to-ensure-the-functioning-of-agricultural-energy-complexes