Deep learning analysis provides a prediction at the individual level for graph *classification* and *regression* tasks. The following are the deep-learning models we have implemented:

Deep neural network

**Deep neural network**(DNN) is a supervised learning algorithm that learns a function by training on a dataset of a number of dimensions. It is composed of fully-connected layers and can learn a non-linear function to approximate either classification or regression. DNN can be trained on either the

*adjacency matrices*or the

*graph measures*. Before training a DNN model, feature selection on the input data can be done by mutual information analysis in our pipelines.

Convolutional neural network

**Convolutional neural network**(CNN) is a deep learning architecture that learns to extract the feature directly from data. This kind of neural network is particularly useful for finding patterns and classifying data effectively. In our pipelines, a CNN model can be training on either the

*adjacency matrices*or the

*binodal graph measures*.

Graph neural network

**Graph neural network**(GNN) is an extension of a CNN that takes

*node features*(e.g. degree, distance) and the

*adjacency matrices*as input to perform classification. During the training process, the multi-head self-attention mechanism is implemented in our pipelines.