Towards Data-Centric Graph Learning for Real-World Applications

In today's data-driven world, graphs play a crucial role in representing a significant portion of big data across diverse domains. However, real-world graph data often presents challenges in terms of quality. This talk emphasizes the need for data-centric graph learning, which has been overlooked in favor of model-centric approaches. We showcase recent advancements in recommender systems, neuroimaging, and drug discovery, highlighting the importance of data-centric approaches to address data quality challenges across diverse domains. By tackling data quality issues and optimizing graph datasets, we can significantly enhance the performance of graph neural networks in real-world applications. The talk concludes with insights into future directions and potential advancements in the field.