International Journal of Scientific Research and Engineering Development

Graph Neural Networks (GNNs) are among the most significant tools in modern machine learning to carry out structured reasoning on systems like molecules, social networks, and knowledge graphs. The paper discusses the major algorithms for graphs-learning: spectral, message passing, and attention mechanisms, along with training procedures that help with scaling graphs-learning and making it robust. It explains the distinctions between the main classes (GCNs, GATs, GraphSAGE, MPNNs), as well as the primary issues that lie in oversmoothing, scalability, interpretability, and adversarial robustness. Soon after, it surveys some next-generation topics like dynamic/temporal GNNs, self-supervised learning, multimodal integration, and quantum-inspired models that will serve as a guide for both the student and the researcher.