Unlocking the Power of Graph Neural Networks—A Systematic Literature Review of Application-Oriented GNN Studies

Imran Ahsan¹, Muhammad Waseem Anwar², JungYoon Kim³, Mucheol Kim^1,4,*
1 Department of Smart City, Chung-Ang University, Seoul, Republic of Korea
2 Department for Innovation, Design and Technology-IDT, Mälardalen University, Eskilstuna, Sweden
3 Department of Game Media, College of Future Industry, Gachon University, Seongnam, Republic of Korea
4 School of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea
* Corresponding Author: Mucheol Kim. Email: email
(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.080382

Received 20 March 2026; Accepted 01 June 2026; Published online 07 July 2026

Abstract

Graph neural networks (GNNs) have demonstrated promising results in enhancing machine learning and artificial intelligence techniques by addressing graph-related problems, including graph categorization, edge prediction, and node prediction. Despite the rapid growth of the field, a structured synthesis of recent application-oriented GNN research is necessary. This systematic literature review analyzes 363 peer-reviewed, application-oriented GNN studies published from 2019 through February 2026 from four scientific repositories. These studies are organized by GNN model variant, (including single and multimodel approaches), and four classification task categories: graph, link, node, and node and edge combined. In addition, the review assesses the use of learning settings, graph types, loss functions, aggregation mechanisms, embedding-representation models, implementation toolkits, datasets, and within-study base-model comparisons. In the reviewed corpus, the graph convolutional network (GCN) is the most common single-model family, which is most often associated with favorable within-study comparisons. In contrast, the model combining GCN with a graph attention network (GAT) is the most frequent multimodel configuration and is most often associated with favorable within-study comparisons. The reviewed corpus yields three broader insights: 1) multimodel architectures have become increasingly prominent after 2023, 2) heterogeneous graphs and a few broad-spectrum backbone families define a wide range of cross-domain applications, and 3) reporting transparency improves unevenly across implementation-oriented studies. This study aims to answer critical research questions regarding the analysis of dataset evolution, feature trends, and learning strategies in GNNs. The findings suggest that future GNN research should report learning settings, implementation toolkits, auxiliary representation models, and layer usage more systematically and evaluate multimodel proposals against strong, clearly identified single-model baselines. These findings provide valuable insight for researchers and practitioners in the field.