Graph Learning as a Foundation for Large-scale Applications

Learn how graph learning serves as a foundational technology for large-scale applications in this 57-minute conference talk presented at ICBS2025 by Zhitao Ying from BIMSA. Explore the theoretical foundations of graph learning and discover how these principles can be applied to solve complex, real-world problems at scale. Gain insights into the latest developments in graph neural networks, their architectural designs, and their practical implementations across various domains. Understand the challenges and opportunities in scaling graph learning algorithms for industrial applications, including computational efficiency, memory optimization, and distributed processing techniques. Examine case studies and examples that demonstrate the effectiveness of graph learning approaches in handling large datasets and complex network structures. Discover how graph learning methodologies can be integrated into existing machine learning pipelines and their potential impact on future technological developments.