Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters [article]

Takanori Maehara, Hoang NT
2021 arXiv   pre-print
Theoretical analyses for graph learning methods often assume a complete observation of the input graph. Such an assumption might not be useful for handling any-size graphs due to the scalability issues in practice. In this work, we develop a theoretical framework for graph classification problems in the partial observation setting (i.e., subgraph samplings). Equipped with insights from graph limit theory, we propose a new graph classification model that works on a randomly sampled subgraph and
more » ... novel topology to characterize the representability of the model. Our theoretical framework contributes a theoretical validation of mini-batch learning on graphs and leads to new learning-theoretic results on generalization bounds as well as size-generalizability without assumptions on the input.
arXiv:2111.03317v1 fatcat:aqw4ljok6rhpdfikuxekqn2q5e