LGP: Few-Shot Class-Evolutionary Learning on Dynamic Graphs

Tiancheng Huang, Feng Zhao, Donglin Wang

Abstract: Graph few-shot learning aims to learn how to quickly adapt to new tasks using only a few labeled data, which transfers learned knowledge of base classes to novel classes. Existing methods are mainly designed for static graphs, while many real-world graphs are dynamic and evolving over time, resulting in a phenomenon of structure and class evolutions. To address the challenges caused by the phenomenon, in this paper, we propose a novel algorithm named Learning to Generate Parameters (LGP) to deal with few-shot class-evolutionary learning on dynamic graphs. Specifically, for the structure evolution, LGP integrates ensemble learning into a backbone network to effectively learn invariant representation across different snapshots within a dynamic graph. For the class evolution, LGP adopts a meta-learning strategy that can learn to generate the classified parameters of novel classes via the parameters of the base classes. Therefore, LGP can quickly adapt to new tasks on a combination of base and novel classes. Besides, LGP utilizes an attention mechanism to capture the evolutionary pattern between the novel and based classes. Extensive experiments on a real-world dataset demonstrate the effectiveness of LGP.