How to use deep learning for data with a feature network structure?

Question

I have a general problem in the application domain.The data contains a high dimensional feature space with a small sample.A sparse network with its node as different features are available.The network has edges.The larger the edge, the higher the correlation or dependence the pair of features have. Generally how I can employ the network information in my model? Currently I searched in the literature.I find the general approach contains: 1.network embedding.To make use of the network information to obtain an embedding of the features. 2.graph neural network.Like GCN (graph convolutional neural network) or GAT(graph attention neural network) or other message passing neural network. The question is that what is the general approach a data scientist can have a try, to make use of the network information on the features? The network is not on different samples, just on the features. JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

The first thing that would come to my mind is to check the feature correlation using the network, and remove highly correlated features before training.