GRAPH NEURAL NETWORKS: FUNDAMENTALS, APPLICATIONS, AND RESEARCH DIRECTIONS

Graph Neural Networks (GNNs) have emerged as an important class of deep learning models for analyzing graph-structured data. Unlike conventional neural networks that primarily operate on regular grids or sequential inputs, GNNs learn representations by combining information about individual entities and their relationships. This capability makes them suitable for applications involving social networks, recommendation systems, biological networks, knowledge graphs, traffic prediction, and fraud detection. This paper presents a narrative review of the fundamental principles, architectures, applications, and research challenges associated with GNNs. It discusses message-passing mechanisms, graph convolutional networks, graph attention networks, GraphSAGE, graph autoencoders, and spatiotemporal graph neural networks. The paper also examines key limitations, including oversmoothing, oversquashing, computational scalability, interpretability, and robustness. Emerging research directions such as self-supervised graph learning, graph transformers, dynamic graph modeling, and explainable graph learning are discussed. The review highlights the importance of selecting appropriate graph representations, architectures, and evaluation methods according to the characteristics of the underlying problem. Continued research in these areas can improve the reliability, efficiency, and practical applicability of graph-based learning systems.