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Graph Machine Learning (GraphML), whereby classical machine learning isgeneralized to irregular graph domains, has enjoyed a recent renaissance,leading to a dizzying array of models and their applications in severaldomains. With its growing applicability to sensitive domains and regulations bygovernmental agencies for trustworthy AI systems, researchers have startedlooking into the issues of transparency and privacy of graph learning.  However, these topics have been mainly investigated independently. In thisposition paper, we provide a unified perspective on the interplay of privacyand transparency in GraphML. In particular, we describe the challenges andpossible research directions for a formal investigation of privacy-transparencytradeoffs in GraphML.

Privacy and Transparency in Graph Machine Learning: A Unified Perspective