Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
This work addresses the problem of catastrophic forgetting for researchers in graph representation learning, but it is incremental as it applies existing methods to a new domain.
The paper tackled catastrophic forgetting in deep graph networks by evaluating classical continual learning techniques on graph data, finding that replay combined with regularization was the most effective strategy.
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 investigation 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.