Metric Based Few-Shot Graph Classification

Abstract

Few-shot graph classification is a novel yet promising emerging research field that still lacks the soundness of well-established research domains. Existing works often consider different benchmarks and evaluation settings, hindering comparison and, therefore, scientific progress. In this work, we start by providing an extensive overview of the possible approaches to solving the task, comparing the current state-of-the-art and baselines via a unified evaluation framework. Our findings show that while graph-tailored approaches have a clear edge on some distributions, easily adapted few-shot learning methods generally perform better. In fact, we show that it is sufficient to equip a simple metric learning baseline with a state-of-the-art graph embedder to obtain the best overall results. We then show that straightforward additions at the latent level lead to substantial improvements by introducing i) a task-conditioned embedding space ii) a MixUp-based data augmentation technique. Finally, we release a highly reusable codebase to foster research in the field, offering modular and extensible implementations of all the relevant techniques.

Publication
Proceedings of the First Learning on Graphs Conference
Donato Crisostomi
Donato Crisostomi
PhD Student

PhD student @ Sapienza, University of Rome | former Applied Science intern @ Amazon Search, Luxembourg | former Research Science intern @ Amazon Alexa, Turin

Valentino Maiorca
Valentino Maiorca
PhD Student

PhD student @ Sapienza, University of Rome

Luca Moschella
Luca Moschella
PhD Student

PhD Student @SapienzaRoma CS | Intern @NVIDIA Toronto Lab | @NNAISENSE

Riccardo Marin
Riccardo Marin
PostDoctoral Researcher
Emanuele Rodolà
Emanuele Rodolà
Full Professor