The present disclosure provides a method for training a Graph Neural Network (GNN) model to perform a task of classifying nodes of a graph based on semi-supervised learning. The method comprises: sampling a batch of labeled nodes and a batch of unlabeled nodes from the nodes of the graph, wherein the graph comprising nodes represented by a feature matrix and edges represented by an adjacency matrix, each of the nodes of graph being represented by a corresponding feature vector of the feature matrix; obtaining a plurality of augmented feature vectors for each node in the batch of labeled nodes and the batch of unlabeled nodes by randomly propagating feature vectors of neighboring nodes of the node based on an adjacency vector of the node; obtaining a plurality of classification predictions for each node in the batch of labeled nodes and the batch of unlabeled nodes by respectively applying the plurality of augmented feature vectors of the node to the GNN model; obtaining a loss based on the classification predictions of the nodes in the batch of labeled nodes and the batch of unlabeled nodes; and updating parameters of the GNN model based on the loss.