  When handling streaming graphs, existing graph representation learning models
encounter a catastrophic forgetting problem, where previously learned knowledge
of these models is easily overwritten when learning with newly incoming graphs.
In response, Continual Graph Learning (CGL) emerges as a novel paradigm
enabling graph representation learning from streaming graphs. Our prior work,
Condense and Train (CaT) is a replay-based CGL framework with a balanced
continual learning procedure, which designs a small yet effective memory bankn
for replaying. Although the CaT alleviates the catastrophic forgetting problem,
there exist three issues: (1) The graph condensation only focuses on labelled
nodes while neglecting abundant information carried by unlabelled nodes; (2)
The continual training scheme of the CaT overemphasises on the previously
learned knowledge, limiting the model capacity to learn from newly added
memories; (3) Both the condensation process and replaying process of the CaT
are time-consuming. In this paper, we propose a PsUdo-label guided Memory bAnk
(PUMA) CGL framework, extending from the CaT to enhance its efficiency and
effectiveness by overcoming the above-mentioned weaknesses and limits. To fully
exploit the information in a graph, PUMA expands the coverage of nodes during
graph condensation with both labelled and unlabelled nodes. Furthermore, a
training-from-scratch strategy is proposed to upgrade the previous continual
learning scheme for a balanced training between the historical and the new
graphs. Besides, PUMA uses a one-time prorogation and wide graph encoders to
accelerate the graph condensation and the graph encoding process in the
training stage to improve the efficiency of the whole framework. Extensive
experiments on six datasets for the node classification task demonstrate the
state-of-the-art performance and efficiency over existing methods.
