Graph Convolutional Networks - Advances and Applications: Exploring advances and applications of graph convolutional networks (GCNs) for learning representations of graph-structured data

Abstract

Graph Convolutional Networks (GCNs) have emerged as a powerful tool for learning representations of graph-structured data. This paper provides an overview of the advances in GCNs and their applications across various domains. We discuss the underlying principles of GCNs, including message passing and graph convolutions, and highlight recent advancements such as attention mechanisms and graph attention networks. We then explore the diverse applications of GCNs, including node classification, link prediction, and graph generation, in fields such as social networks, biology, and recommendation systems. Finally, we discuss challenges and future directions in GCN research, including scalability, interpretability, and robustness.