  This paper addresses the challenge of incremental learning in growing graphs
with increasingly complex tasks. The goal is to continually train a graph model
to handle new tasks while retaining its inference ability on previous tasks.
Existing methods usually neglect the importance of memory diversity, limiting
in effectively selecting high-quality memory from previous tasks and
remembering broad previous knowledge within the scarce memory on graphs. To
address that, we introduce a novel holistic Diversified Memory Selection and
Generation (DMSG) framework for incremental learning in graphs, which first
introduces a buffer selection strategy that considers both intra-class and
inter-class diversities, employing an efficient greedy algorithm for sampling
representative training nodes from graphs into memory buffers after learning
each new task. Then, to adequately rememorize the knowledge preserved in the
memory buffer when learning new tasks, we propose a diversified memory
generation replay method. This method first utilizes a variational layer to
generate the distribution of buffer node embeddings and sample synthesized ones
for replaying. Furthermore, an adversarial variational embedding learning
method and a reconstruction-based decoder are proposed to maintain the
integrity and consolidate the generalization of the synthesized node
embeddings, respectively. Finally, we evaluate our model on node classification
tasks involving increasing class numbers. Extensive experimental results on
publicly accessible datasets demonstrate the superiority of DMSG over
state-of-the-art methods.
