Node Classification on The Citation Network Using Graph Neural Network

Abstract

Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural network approaches, we determine the accuracy of each method. In the baseline experiment, training and testing were performed using the NN approach. The resulting accuracy of FNN was 72.59 % and GNN model has increased by 81.65 %. There is a 9.06 % increase in accuracy between the baseline model and the GNN model. The new data utilized in the model predictions showcases the probabilities of each class through randomly generated examples.