Enhancing Node Classification: Integrating Particle Swarm Optimization with Graph Convolutional Networks

The effectiveness of Graph Neural Networks (GNNs) in processing graph-structured data gained widespread recognition, and these models have applications in a wide range of fields. Even though GNNs have a lot of potential, optimising GNN models for particular tasks is still challenging. Within the scope of this research, a method is presented that combines Particle Swarm Optimization (PSO) and Graph Convolutional Networks (GCN) to enhance the performance of node classification. Using the Amazon Computers and Amazon Photo datasets, which are both made up of product nodes that are connected to one another. The methodology includes data preprocessing with PyTorch Geometric, strategic data partitioning, and a robust hyperparameter optimization process using PSO. The modification was able to achieve an accuracy of 88.7% on the Amazon Computers dataset and 94.9% on the Amazon Photo dataset, which is higher than the traditional GCN baselines. Our GCN-PSO model demonstrated significant performance enhancements. These findings highlight the effectiveness of PSO in fine-tuning GCN hyperparameters, which ultimately results in outcomes that are more accurate and efficient. When it comes to improving graph-based data analysis, the research highlights the potential of combining GCNs with optimization techniques such as PSO. This method could be extended to other graph learning challenges in subsequent research, and alternative optimization strategies could be investigated to further push the limits of GNN performance.