  Many real-world graph learning tasks require handling dynamic graphs where
new nodes and edges emerge. Dynamic graph learning methods commonly suffer from
the catastrophic forgetting problem, where knowledge learned for previous
graphs is overwritten by updates for new graphs. To alleviate the problem,
continual graph learning methods are proposed. However, existing continual
graph learning methods aim to learn new patterns and maintain old ones with the
same set of parameters of fixed size, and thus face a fundamental tradeoff
between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN)
for continual learning on dynamic graphs that circumvents the tradeoff via
parameter isolation and expansion. Our motivation lies in that different
parameters contribute to learning different graph patterns. Based on the idea,
we expand model parameters to continually learn emerging graph patterns.
Meanwhile, to effectively preserve knowledge for unaffected patterns, we find
parameters that correspond to them via optimization and freeze them to prevent
them from being rewritten. Experiments on eight real-world datasets corroborate
the effectiveness of PI-GNN compared to state-of-the-art baselines.
