NeurIPS 2025 Workshop on New Perspectives in Advancing Graph Machine Learning

Graphs serve as a powerful representational framework for machine learning, and their integration has substantially advanced the field. Indeed, extensive studies have pushed forward graph machine learning (GML) in both theory and applications. Recently, new perspectives have been emerging in the machine learning community, including algebraic–topological analyses, foundation models, generative models, and large models in applications. Leveraging these ideas for core graph machine learning holds a lot of promise, including the dual benefit of deeper theoretical insight, new capabilities and more powerful, application-aligned algorithms and models. The aim of this workshop is to explore and connect these new perspectives on GML, and to identify overarching challenges and tools – in terms of theory, methodology, and modeling.