Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark

Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an
more » ... stigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments.
doi:10.3389/frai.2022.824655 pmid:35187476 pmcid:PMC8855050 fatcat:35t7jak5qrhere77ohpnjuvheq