| Graph Neural Networks (GNNs) are specialized neural networks that operate on graph-structured data, utilizing the connections between nodes to learn and process information. To achieve optimal performance, GNNs require the automatic selection of the best loss and optimization functions, which allows the model to adapt to the unique features of the dataset being used. This eliminates the need for manual selection, saving time and minimizing the requirement for domain-specific knowledge. The automatic selection of loss and optimization functions is a crucial factor in achieving state-of-the-art results when training GNNs. In this study, we trained Graph Convolutional Networks (GCNs) and Graph Attention Networks (GAT) models for node classification on three benchmark datasets. To automatically select the best loss and optimization functions, we utilized performance metrics. We implemented a learning rate scheduler to adjust the learning rate based on the model's performance, which led to improved results. We evaluated the model's performance using multiple metrics and reported the best loss function and performance metric, enabling users to compare its performance to other models. Our approach achieved state-of-the-art results, highlighting the importance of selecting the appropriate loss and optimizer functions. Additionally, we developed a real-time visualization of the GCN model during training, providing users with a detailed understanding of the model's behavior. Overall, this study provides a comprehensive understanding of GNNs and their application to graph-structured data, with a specific focus on real-time visualization of GNN behavior during training. |
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