Graphs are ubiquitous data structures providing powerful representations for objects with interactions. Empowered by recent progress in AI and machine learning, rapid technical progress has been achieved in graph mining. On the other hand, research and clinical practices in public health have generated large volumes of interconnected data, where the exploration of modern graph mining principles and techniques are still rather limited.
In this talk, Carl Yang introduces his team's research vision and agenda towards graph mining for health, followed by success examples from our recent exploration on multimodality graph construction, trustworthy graph modeling, and federated graph learning. He will conclude the talk with discussions on future directions that can benefit from further collaborations with researchers interested in data mining or health informatics in general.
Presenter
