Learning on Graphs with Missing Node Features

A new paper and associated blogpost by Emanuele Rossi and Prof. Michael Bronstein.

Most Graph Neural Networks typically run under the assumption of a full set of features available for all nodes. In real-world scenarios features are often only partially available (for example, in social networks, age and gender can be known only for a small subset of users). Feature Propagation is an efficient and scalable approach for handling missing features in graph machine learning applications that works surprisingly well despite its simplicity.

📝 Blog Post: https://bit.ly/3ILn1Rl
💻 Code: https://bit.ly/3J9ftbr
🎥 Recording: https://bit.ly/3CbBvHW
📖 Slides: https://bit.ly/3Mh5geW
📜 Paper: https://bit.ly/3Kgo4JE