A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

• Published in: Journal of big data 2024-01, Vol.11 (1), p.18-43, Article 18
• Main Authors: Khemani, Bharti, Patil, Shruti, Kotecha, Ketan, Tanwar, Sudeep
• Format: Article
• Language: English
• Subjects: Big Data; Case Study; Cognitive tasks; Communications Engineering; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; Datasets; Deep learning; Graph Attention Networks (GAT); Graph Convolution Network (GCN); Graph Neural Network (GNN); Graph neural networks; Graph theory; GraphSAGE; Information Storage and Retrieval; Machine learning; Mathematical Applications in Computer Science; Message passing; Message Passing Mechanism; Natural Language Processing (NLP); Networks; Neural networks; Nodes; Standard data
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-023-00876-4

Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures. Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. It also discusses the message-passing mechanism employed by GNN models and examines the strengths and limitations of these models in different domains. Furthermore, the paper explores the diverse applications of GNNs, the datasets commonly used with them, and the Python libraries that support GNN models. It offers an extensive overview of the landscape of GNN research and its practical implementations.