Graph Neural Networks (GNNs) have shown potential in various domains, including databases and distributed stream processing systems. However, the effectiveness and accuracy of GNN models, particularly in zero-shot learning scenarios, heavily depend on selecting appropriate features. This thesis aims to develop and evaluate feature selection strategies and an optimized method to select from feature selection strategies based on constraints that enhance the performance of zero-shot-based GNN models in the context of parallel stream processing.
Key Objectives
- Identify and analyze the key features that impact the performance of GNN models in parallel stream processing tasks.
- Develop a systematic feature selection strategy that optimizes the zero-shot learning capabilities of GNN models in distributed stream processing environments.
- Evaluate the effectiveness of the proposed feature selection strategy in improving the accuracy, efficiency, and scalability of zero-shot GNN models.