A Comparative Study on Node Classification Methods For Undirected Social Networks

Traditional Neural Networks can solve problems with normal 1-dimensional and 2-dimensional Euclidean data such as image and text classification. However, most real-life problems are relationship-based and the corresponding real-world data has a non-Euclidean nature. This paper presents a comparative study between Graph Neural Networks (GNNs) and traditional approaches (K-Nearest Neighbour(KNN)) for solving real-life problems involving non-Euclidean data with complex relationships. The results demonstrate that GNN models outperform KNN in terms of accuracy while maintaining low runtime on graph structured data. The study highlights the strengths and limitations of Graph Convolutional Networks (GCNs) and GraphSAGE, with GraphSAGE offering flexibility and scalability but potentially introducing aggregation bias. Ongoing experiments include investigating the sensitivity of the algorithms to hyperparameter values. Overall, this research contributes to the understanding and practical application of GNN methodologies in various domains.