  Although most graph neural networks (GNNs) can operate on graphs of any size,
their classification performance often declines on graphs larger than those
encountered during training. Existing methods insufficiently address the
removal of size information from graph representations, resulting in
sub-optimal performance and reliance on backbone models. In response, we
propose DISGEN, a novel and model-agnostic framework designed to disentangle
size factors from graph representations. DISGEN employs size- and
task-invariant augmentations and introduces a decoupling loss that minimizes
shared information in hidden representations, with theoretical guarantees for
its effectiveness. Our empirical results show that DISGEN outperforms the
state-of-the-art models by up to 6% on real-world datasets, underscoring its
effectiveness in enhancing the size generalizability of GNNs. Our codes are
available at: https://github.com/GraphmindDartmouth/DISGEN.
