  Continual graph learning (CGL) studies the problem of learning from an
infinite stream of graph data, consolidating historical knowledge, and
generalizing it to the future task. At once, only current graph data are
available. Although some recent attempts have been made to handle this task, we
still face two potential challenges: 1) most of existing works only manipulate
on the intermediate graph embedding and ignore intrinsic properties of graphs.
It is non-trivial to differentiate the transferred information across graphs.
2) recent attempts take a parameter-sharing policy to transfer knowledge across
time steps or progressively expand new architecture given shifted graph
distribution. Learning a single model could loss discriminative information for
each graph task while the model expansion scheme suffers from high model
complexity. In this paper, we point out that latent relations behind graph
edges can be attributed as an invariant factor for the evolving graphs and the
statistical information of latent relations evolves. Motivated by this, we
design a relation-aware adaptive model, dubbed as RAM-CG, that consists of a
relation-discovery modular to explore latent relations behind edges and a
task-awareness masking classifier to accounts for the shifted. Extensive
experiments show that RAM-CG provides significant 2.2%, 6.9% and 6.6% accuracy
improvements over the state-of-the-art results on CitationNet, OGBN-arxiv and
TWITCH dataset, respective.
