Fully-inductive Node Classification on Arbitrary Graphs

Understand

One fundamental challenge in graph machine learning is generalizing to new graphs.

• Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones.
• This paper introduces a fully-inductive setup, where models should perform inference on arbitrary test graphs with new structures, feature and label spaces.
• We propose GraphAny as the first attempt at this challenging setup.