Before revision:  To tackle these issues, we take advantage of image processing in agility and present Node2Img , a flexible architecture for large-scale graph learning. 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:  To tackle these issues, we take advantage of image processing in agility and present Node2Img , a flexible uncoupled training framework that leverages the independent mapped data for obtaining the embedding . 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.