A Survey of Graph Neural Networks: Architectures, Applications, and Challenges
Abstract
Graph neural networks (GNNs) have emerged as a powerful framework for learning from structured data such as graphs and networks. This review paper provides an overview of GNN architectures, including graph convolutional networks (GCNs), graph attention networks (GATs), and graph recurrent networks (GRNs). It explores the applications of GNNs in social networks, bioinformatics, and recommendation systems, along with challenges and future research directions.
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