Table 8 Different application areas with their proposed methodology of Graph Neural Networks
Ref | Application Area | Proposed Methodology | GNN Model applied |
|---|---|---|---|
Recommender Systems | User/item representations Recommendation system based on heterogeneous features | GCN, GAT, GraphSAGE | |
(2021) | Natural Language Processing | Text graph transformer for document classification Text-Based Relational Reasoning Semantic parsing | graph2seq, graph2tree, graph2graph |
[63], (2021) [64] (2022) | HealthCare | Data Analysis-Based Agricultural Products Management Immunization and vaccine injury | GCN |
[72] (2017) [73] (2020) | Natural Language Processing | Knowledge Base Completion of text Knowledge Graph Alignment of text | GNN-LSTM |
[67], (2021) [68] (2020) | Computer Vision | Image and video understanding 3D object detection in a point cloud | GCN, GAT |
[76] (2021) | Anomaly Detection | Industrial Internet of Things | GCN, GAN, and GraphSAGE |
[29] (2023) | Traffic Forecasting | Hybrid GCN and branch-and-bound optimization for traffic flow forecasting | GCN |
[77] (2023) | HealthCare | The configuration of fMRI-derived networks determines the effectiveness of a graph neural network in discerning patients with major depressive disorder through classification | GNN (Text based) |
[28] (2023) | Traffic Prediction | A study focusing on the prediction of multi-port ship traffic through the application of Spatiotemporal Graph Neural Networks | GNN |