Before revision:  Node2Img maps the nodes to "images" (i.e. grid-like data in Euclidean space) which can be the inputs of Convolutional Neural Network (CNN) . Instead of leveraging the fixed whole network as a batch to train the model, Node2Img supports a more efficacious framework in practice, where the batch size can be set elastically and the data in the same batch can be calculated parallelly.
After revision:  Node2Img maps the nodes to "images" (i.e. grid-like data in Euclidean space) which can be the inputs of Convolutional Neural Network (CNN) . Instead of leveraging the fixed whole network as a batch to train the model, Node2Img supports a more efficacious framework in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN). This simple but valid strategy significantly saves memory and computational resource while achieving comparable results with the leading GCN-based models.