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.

连续图学习研究了从无限的图数据流中学习的问题，将历史知识整合并推广到未来的任务。在当前只有当前的图数据可用。本文提出了一种关系感知自适应模型，并说明了图边缘后面的潜在关系可以被归因为不变因素。通过实验证明，该模型在 CitationNet、OGBN-arxiv 和 TWITCH 数据集上相对于现有方法分别提高了 2.2%、6.9% 和 6.6% 的准确度。