Graph Neural Networks: A Comprehensive Review of Models, Applications, and Challenges

Abstract

This paper provides a comprehensive review of Graph Neural Networks (GNNs), a burgeoning field at the intersection of machine learning and graph theory. GNNs have emerged as powerful tools for learning representations of graph-structured data, revolutionizing various domains including social network analysis, bioinformatics, and recommendation systems. Beginning with an overview of traditional graph analysis methods and their limitations, we delve into the foundational principles of GNNs, highlighting key architectures such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). We survey recent advancements and discuss applications across diverse fields, from cybersecurity to urban planning. Furthermore, we address critical challenges such as scalability, interpretability, and generalization, proposing future research directions to enhance the robustness and applicability of GNNs in real-world scenarios. This review aims to consolidate existing knowledge, identify gaps, and inspire continued innovation in the evolving landscape of graph-based machine learning.