A Dynamic Graph Learning Method with Backward Knowledge Feedback via Parameter Expansion

Dynamic graphs play a crucial role in modeling temporal interaction systems in the real world. Current dynamic graph learning methods suffer from limited representation capability due to the following issues: (1) fixed model parameter capacity leads to knowledge squeezing when continuously learning new graph knowledge; (2) such methods adhere to the fundamental paradigm of leveraging historical graph knowledge to learn new graph knowledge, neglecting the potential of backward knowledge feedback for enhancing the learning of historical graph knowledge. To address these issues, we propose a novel Dynamic graph learning method with Backward Knowledge Feedback via Parameter Expansion (DBKF-PE), which achieves effective representation of dynamic graphs through a bidirectional knowledge circulation structure. The circulation structure consists of forward knowledge injection and backward knowledge feedback. Specifically, the forward knowledge injection incorporates a new graph-aware parameter expansion strategy. This strategy expands new parameters to learn new graph knowledge while freezing old parameters, thereby preventing conflicts between new and historical graph knowledge in the parameter space. Then, the backward knowledge feedback is modeled as a meta-learning optimization objective, which not only alleviates catastrophic forgetting through the proposed backward knowledge feedback mechanism but also further enhances the performance of the new model on historical graphs. Finally, extensive experiments on six datasets validate that DBKF-PE outperforms the state-of-the-art baselines.