Graph Convolutional Networks (GCNs) are a specialized class of Graph Neural Networks designed to perform convolution operations on graph-structured data, enabling effective feature extraction and representation learning over nodes and their neighborhoods. Unlike traditional convolutional networks, GCNs aggregate and transform information from a node’s neighbors according to the graph topology, capturing both local and global structural patterns. Early work, such as the spectral-based GCN by Kipf and Welling, laid the foundation for semi-supervised node classification and link prediction. Subsequent research has extended GCNs to handle heterogeneous graphs, dynamic graphs, multi-relational graphs, and large-scale networks, integrating attention mechanisms, residual connections, and graph sampling strategies for scalability. Applications span social network analysis, recommendation systems, knowledge graphs, bioinformatics (e.g., protein–protein interactions, drug discovery), traffic prediction, and cybersecurity. Current research also explores self-supervised and contrastive learning, robustness to adversarial attacks, interpretability, and integration with transformers for graph data, establishing GCNs as a core methodology for learning from structured and relational data.