Abstract
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.
This research was supported by the research training group “Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis) funded by the German federal state of North Rhine-Westphalia and the project SAIL. SAIL is funded by the Ministry of Culture and Science of the State of North Rhine-Westphalia under grant no. NW21-059B.
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Code availability. https://github.com/Rao-Sanaullah/GNN-Classification-with-Automatic-Loss-Function-and-Optimizer-Selection Accessed Apr 2023
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In this study, we have made the code used in our experiments publicly available on GitHub [15]. This allows other researchers to replicate our experiments and build upon our work and to ensure the reproducibility of our results, we have used publicly available datasets for generating all the test cases.
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Sanaullah, Koravuna, S., Rückert, U., Jungeblut, T. (2023). Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_17
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DOI: https://doi.org/10.1007/978-3-031-34204-2_17
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