  We investigate the replay buffer in rehearsal-based approaches for graph
continual learning (GCL) methods. Existing rehearsal-based GCL methods select
the most representative nodes for each class and store them in a replay buffer
for later use in training subsequent tasks. However, we discovered that
considering only the class representativeness of each replayed node makes the
replayed nodes to be concentrated around the center of each class, incurring a
potential risk of overfitting to nodes residing in those regions, which
aggravates catastrophic forgetting. Moreover, as the rehearsal-based approach
heavily relies on a few replayed nodes to retain knowledge obtained from
previous tasks, involving the replayed nodes that have irrelevant neighbors in
the model training may have a significant detrimental impact on model
performance. In this paper, we propose a GCL model named DSLR, specifically, we
devise a coverage-based diversity (CD) approach to consider both the class
representativeness and the diversity within each class of the replayed nodes.
Moreover, we adopt graph structure learning (GSL) to ensure that the replayed
nodes are connected to truly informative neighbors. Extensive experimental
results demonstrate the effectiveness and efficiency of DSLR. Our source code
is available at https://github.com/seungyoon-Choi/DSLR_official.
